{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "b9527d47",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                 \r"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import efinance as ef\n",
    "import re\n",
    "import numpy as np\n",
    "from scipy.optimize import curve_fit\n",
    "import matplotlib.pyplot as plt\n",
    "import akshare as ak\n",
    "fund_fh_em_df = ak.fund_fh_em()\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "149edd96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['159001.SZ.xlsx',\n",
       " '159003.SZ.xlsx',\n",
       " '159005.SZ.xlsx',\n",
       " '159501.SZ.xlsx',\n",
       " '159502.SZ.xlsx',\n",
       " '159503.SZ.xlsx',\n",
       " '159506.SZ.xlsx',\n",
       " '159507.SZ.xlsx',\n",
       " '159508.SZ.xlsx',\n",
       " '159509.SZ.xlsx',\n",
       " '159510.SZ.xlsx',\n",
       " '159511.SZ.xlsx',\n",
       " '159512.SZ.xlsx',\n",
       " '159513.SZ.xlsx',\n",
       " '159515.SZ.xlsx',\n",
       " '159516.SZ.xlsx',\n",
       " '159517.SZ.xlsx',\n",
       " '159519.SZ.xlsx',\n",
       " '159520.SZ.xlsx',\n",
       " '159521.SZ.xlsx',\n",
       " '159522.SZ.xlsx',\n",
       " '159523.SZ.xlsx',\n",
       " '159531.SZ.xlsx',\n",
       " '159532.SZ.xlsx',\n",
       " '159535.SZ.xlsx',\n",
       " '159536.SZ.xlsx',\n",
       " '159537.SZ.xlsx',\n",
       " '159538.SZ.xlsx',\n",
       " '159539.SZ.xlsx',\n",
       " '159540.SZ.xlsx',\n",
       " '159541.SZ.xlsx',\n",
       " '159543.SZ.xlsx',\n",
       " '159546.SZ.xlsx',\n",
       " '159551.SZ.xlsx',\n",
       " '159560.SZ.xlsx',\n",
       " '159601.SZ.xlsx',\n",
       " '159602.SZ.xlsx',\n",
       " '159603.SZ.xlsx',\n",
       " '159605.SZ.xlsx',\n",
       " '159606.SZ.xlsx',\n",
       " '159607.SZ.xlsx',\n",
       " '159608.SZ.xlsx',\n",
       " '159609.SZ.xlsx',\n",
       " '159610.SZ.xlsx',\n",
       " '159611.SZ.xlsx',\n",
       " '159612.SZ.xlsx',\n",
       " '159613.SZ.xlsx',\n",
       " '159615.SZ.xlsx',\n",
       " '159616.SZ.xlsx',\n",
       " '159617.SZ.xlsx',\n",
       " '159618.SZ.xlsx',\n",
       " '159619.SZ.xlsx',\n",
       " '159620.SZ.xlsx',\n",
       " '159621.SZ.xlsx',\n",
       " '159622.SZ.xlsx',\n",
       " '159623.SZ.xlsx',\n",
       " '159625.SZ.xlsx',\n",
       " '159627.SZ.xlsx',\n",
       " '159628.SZ.xlsx',\n",
       " '159629.SZ.xlsx',\n",
       " '159630.SZ.xlsx',\n",
       " '159631.SZ.xlsx',\n",
       " '159632.SZ.xlsx',\n",
       " '159633.SZ.xlsx',\n",
       " '159635.SZ.xlsx',\n",
       " '159636.SZ.xlsx',\n",
       " '159637.SZ.xlsx',\n",
       " '159638.SZ.xlsx',\n",
       " '159639.SZ.xlsx',\n",
       " '159640.SZ.xlsx',\n",
       " '159641.SZ.xlsx',\n",
       " '159642.SZ.xlsx',\n",
       " '159643.SZ.xlsx',\n",
       " '159645.SZ.xlsx',\n",
       " '159646.SZ.xlsx',\n",
       " '159647.SZ.xlsx',\n",
       " '159649.SZ.xlsx',\n",
       " '159650.SZ.xlsx',\n",
       " '159651.SZ.xlsx',\n",
       " '159652.SZ.xlsx',\n",
       " '159653.SZ.xlsx',\n",
       " '159655.SZ.xlsx',\n",
       " '159656.SZ.xlsx',\n",
       " '159657.SZ.xlsx',\n",
       " '159658.SZ.xlsx',\n",
       " '159659.SZ.xlsx',\n",
       " '159660.SZ.xlsx',\n",
       " '159661.SZ.xlsx',\n",
       " '159662.SZ.xlsx',\n",
       " '159663.SZ.xlsx',\n",
       " '159665.SZ.xlsx',\n",
       " '159666.SZ.xlsx',\n",
       " '159667.SZ.xlsx',\n",
       " '159669.SZ.xlsx',\n",
       " '159670.SZ.xlsx',\n",
       " '159671.SZ.xlsx',\n",
       " '159672.SZ.xlsx',\n",
       " '159673.SZ.xlsx',\n",
       " '159675.SZ.xlsx',\n",
       " '159676.SZ.xlsx',\n",
       " '159677.SZ.xlsx',\n",
       " '159678.SZ.xlsx',\n",
       " '159679.SZ.xlsx',\n",
       " '159680.SZ.xlsx',\n",
       " '159681.SZ.xlsx',\n",
       " '159682.SZ.xlsx',\n",
       " '159683.SZ.xlsx',\n",
       " '159685.SZ.xlsx',\n",
       " '159686.SZ.xlsx',\n",
       " '159687.SZ.xlsx',\n",
       " '159688.SZ.xlsx',\n",
       " '159689.SZ.xlsx',\n",
       " '159690.SZ.xlsx',\n",
       " '159691.SZ.xlsx',\n",
       " '159692.SZ.xlsx',\n",
       " '159693.SZ.xlsx',\n",
       " '159695.SZ.xlsx',\n",
       " '159696.SZ.xlsx',\n",
       " '159697.SZ.xlsx',\n",
       " '159698.SZ.xlsx',\n",
       " '159699.SZ.xlsx',\n",
       " '159701.SZ.xlsx',\n",
       " '159703.SZ.xlsx',\n",
       " '159706.SZ.xlsx',\n",
       " '159707.SZ.xlsx',\n",
       " '159708.SZ.xlsx',\n",
       " '159709.SZ.xlsx',\n",
       " '159710.SZ.xlsx',\n",
       " '159711.SZ.xlsx',\n",
       " '159712.SZ.xlsx',\n",
       " '159713.SZ.xlsx',\n",
       " '159715.SZ.xlsx',\n",
       " '159716.SZ.xlsx',\n",
       " '159717.SZ.xlsx',\n",
       " '159718.SZ.xlsx',\n",
       " '159719.SZ.xlsx',\n",
       " '159720.SZ.xlsx',\n",
       " '159721.SZ.xlsx',\n",
       " '159723.SZ.xlsx',\n",
       " '159725.SZ.xlsx',\n",
       " '159726.SZ.xlsx',\n",
       " '159728.SZ.xlsx',\n",
       " '159729.SZ.xlsx',\n",
       " '159730.SZ.xlsx',\n",
       " '159731.SZ.xlsx',\n",
       " '159732.SZ.xlsx',\n",
       " '159733.SZ.xlsx',\n",
       " '159735.SZ.xlsx',\n",
       " '159736.SZ.xlsx',\n",
       " '159738.SZ.xlsx',\n",
       " '159739.SZ.xlsx',\n",
       " '159740.SZ.xlsx',\n",
       " '159741.SZ.xlsx',\n",
       " '159742.SZ.xlsx',\n",
       " '159743.SZ.xlsx',\n",
       " '159745.SZ.xlsx',\n",
       " '159747.SZ.xlsx',\n",
       " '159748.SZ.xlsx',\n",
       " '159750.SZ.xlsx',\n",
       " '159751.SZ.xlsx',\n",
       " '159752.SZ.xlsx',\n",
       " '159755.SZ.xlsx',\n",
       " '159757.SZ.xlsx',\n",
       " '159758.SZ.xlsx',\n",
       " '159760.SZ.xlsx',\n",
       " '159761.SZ.xlsx',\n",
       " '159763.SZ.xlsx',\n",
       " '159766.SZ.xlsx',\n",
       " '159767.SZ.xlsx',\n",
       " '159768.SZ.xlsx',\n",
       " '159769.SZ.xlsx',\n",
       " '159770.SZ.xlsx',\n",
       " '159773.SZ.xlsx',\n",
       " '159775.SZ.xlsx',\n",
       " '159776.SZ.xlsx',\n",
       " '159777.SZ.xlsx',\n",
       " '159778.SZ.xlsx',\n",
       " '159779.SZ.xlsx',\n",
       " '159780.SZ.xlsx',\n",
       " '159781.SZ.xlsx',\n",
       " '159782.SZ.xlsx',\n",
       " '159783.SZ.xlsx',\n",
       " '159786.SZ.xlsx',\n",
       " '159787.SZ.xlsx',\n",
       " '159788.SZ.xlsx',\n",
       " '159790.SZ.xlsx',\n",
       " '159791.SZ.xlsx',\n",
       " '159792.SZ.xlsx',\n",
       " '159793.SZ.xlsx',\n",
       " '159795.SZ.xlsx',\n",
       " '159796.SZ.xlsx',\n",
       " '159797.SZ.xlsx',\n",
       " '159798.SZ.xlsx',\n",
       " '159801.SZ.xlsx',\n",
       " '159804.SZ.xlsx',\n",
       " '159805.SZ.xlsx',\n",
       " '159806.SZ.xlsx',\n",
       " '159807.SZ.xlsx',\n",
       " '159808.SZ.xlsx',\n",
       " '159810.SZ.xlsx',\n",
       " '159811.SZ.xlsx',\n",
       " '159812.SZ.xlsx',\n",
       " '159813.SZ.xlsx',\n",
       " '159814.SZ.xlsx',\n",
       " '159816.SZ.xlsx',\n",
       " '159819.SZ.xlsx',\n",
       " '159820.SZ.xlsx',\n",
       " '159821.SZ.xlsx',\n",
       " '159822.SZ.xlsx',\n",
       " '159823.SZ.xlsx',\n",
       " '159824.SZ.xlsx',\n",
       " '159825.SZ.xlsx',\n",
       " '159827.SZ.xlsx',\n",
       " '159828.SZ.xlsx',\n",
       " '159830.SZ.xlsx',\n",
       " '159831.SZ.xlsx',\n",
       " '159834.SZ.xlsx',\n",
       " '159835.SZ.xlsx',\n",
       " '159836.SZ.xlsx',\n",
       " '159837.SZ.xlsx',\n",
       " '159838.SZ.xlsx',\n",
       " '159839.SZ.xlsx',\n",
       " '159840.SZ.xlsx',\n",
       " '159841.SZ.xlsx',\n",
       " '159842.SZ.xlsx',\n",
       " '159843.SZ.xlsx',\n",
       " '159845.SZ.xlsx',\n",
       " '159847.SZ.xlsx',\n",
       " '159848.SZ.xlsx',\n",
       " '159849.SZ.xlsx',\n",
       " '159850.SZ.xlsx',\n",
       " '159851.SZ.xlsx',\n",
       " '159852.SZ.xlsx',\n",
       " '159853.SZ.xlsx',\n",
       " '159855.SZ.xlsx',\n",
       " '159856.SZ.xlsx',\n",
       " '159857.SZ.xlsx',\n",
       " '159858.SZ.xlsx',\n",
       " '159859.SZ.xlsx',\n",
       " '159861.SZ.xlsx',\n",
       " '159862.SZ.xlsx',\n",
       " '159863.SZ.xlsx',\n",
       " '159864.SZ.xlsx',\n",
       " '159865.SZ.xlsx',\n",
       " '159866.SZ.xlsx',\n",
       " '159867.SZ.xlsx',\n",
       " '159869.SZ.xlsx',\n",
       " '159870.SZ.xlsx',\n",
       " '159871.SZ.xlsx',\n",
       " '159872.SZ.xlsx',\n",
       " '159873.SZ.xlsx',\n",
       " '159875.SZ.xlsx',\n",
       " '159876.SZ.xlsx',\n",
       " '159877.SZ.xlsx',\n",
       " '159880.SZ.xlsx',\n",
       " '159881.SZ.xlsx',\n",
       " '159883.SZ.xlsx',\n",
       " '159885.SZ.xlsx',\n",
       " '159886.SZ.xlsx',\n",
       " '159887.SZ.xlsx',\n",
       " '159888.SZ.xlsx',\n",
       " '159889.SZ.xlsx',\n",
       " '159890.SZ.xlsx',\n",
       " '159891.SZ.xlsx',\n",
       " '159892.SZ.xlsx',\n",
       " '159895.SZ.xlsx',\n",
       " '159896.SZ.xlsx',\n",
       " '159898.SZ.xlsx',\n",
       " '159899.SZ.xlsx',\n",
       " '159901.SZ.xlsx',\n",
       " '159902.SZ.xlsx',\n",
       " '159903.SZ.xlsx',\n",
       " '159905.SZ.xlsx',\n",
       " '159906.SZ.xlsx',\n",
       " '159907.SZ.xlsx',\n",
       " '159908.SZ.xlsx',\n",
       " '159909.SZ.xlsx',\n",
       " '159910.SZ.xlsx',\n",
       " '159912.SZ.xlsx',\n",
       " '159913.SZ.xlsx',\n",
       " '159915.SZ.xlsx',\n",
       " '159916.SZ.xlsx',\n",
       " '159918.SZ.xlsx',\n",
       " '159919.SZ.xlsx',\n",
       " '159920.SZ.xlsx',\n",
       " '159922.SZ.xlsx',\n",
       " '159923.SZ.xlsx',\n",
       " '159925.SZ.xlsx',\n",
       " '159928.SZ.xlsx',\n",
       " '159929.SZ.xlsx',\n",
       " '159930.SZ.xlsx',\n",
       " '159931.SZ.xlsx',\n",
       " '159933.SZ.xlsx',\n",
       " '159934.SZ.xlsx',\n",
       " '159935.SZ.xlsx',\n",
       " '159936.SZ.xlsx',\n",
       " '159937.SZ.xlsx',\n",
       " '159938.SZ.xlsx',\n",
       " '159939.SZ.xlsx',\n",
       " '159940.SZ.xlsx',\n",
       " '159941.SZ.xlsx',\n",
       " '159943.SZ.xlsx',\n",
       " '159944.SZ.xlsx',\n",
       " '159945.SZ.xlsx',\n",
       " '159948.SZ.xlsx',\n",
       " '159949.SZ.xlsx',\n",
       " '159952.SZ.xlsx',\n",
       " '159954.SZ.xlsx',\n",
       " '159956.SZ.xlsx',\n",
       " '159957.SZ.xlsx',\n",
       " '159958.SZ.xlsx',\n",
       " '159959.SZ.xlsx',\n",
       " '159960.SZ.xlsx',\n",
       " '159961.SZ.xlsx',\n",
       " '159964.SZ.xlsx',\n",
       " '159965.SZ.xlsx',\n",
       " '159966.SZ.xlsx',\n",
       " '159967.SZ.xlsx',\n",
       " '159968.SZ.xlsx',\n",
       " '159969.SZ.xlsx',\n",
       " '159970.SZ.xlsx',\n",
       " '159971.SZ.xlsx',\n",
       " '159972.SZ.xlsx',\n",
       " '159973.SZ.xlsx',\n",
       " '159974.SZ.xlsx',\n",
       " '159975.SZ.xlsx',\n",
       " '159976.SZ.xlsx',\n",
       " '159977.SZ.xlsx',\n",
       " '159980.SZ.xlsx',\n",
       " '159981.SZ.xlsx',\n",
       " '159982.SZ.xlsx',\n",
       " '159983.SZ.xlsx',\n",
       " '159985.SZ.xlsx',\n",
       " '159987.SZ.xlsx',\n",
       " '159990.SZ.xlsx',\n",
       " '159991.SZ.xlsx',\n",
       " '159992.SZ.xlsx',\n",
       " '159993.SZ.xlsx',\n",
       " '159994.SZ.xlsx',\n",
       " '159995.SZ.xlsx',\n",
       " '159996.SZ.xlsx',\n",
       " '159997.SZ.xlsx',\n",
       " '159998.SZ.xlsx',\n",
       " '510010.SH.xlsx',\n",
       " '510020.SH.xlsx',\n",
       " '510030.SH.xlsx',\n",
       " '510050.SH.xlsx',\n",
       " '510060.SH.xlsx',\n",
       " '510090.SH.xlsx',\n",
       " '510100.SH.xlsx',\n",
       " '510130.SH.xlsx',\n",
       " '510150.SH.xlsx',\n",
       " '510160.SH.xlsx',\n",
       " '510170.SH.xlsx',\n",
       " '510180.SH.xlsx',\n",
       " '510190.SH.xlsx',\n",
       " '510200.SH.xlsx',\n",
       " '510210.SH.xlsx',\n",
       " '510230.SH.xlsx',\n",
       " '510270.SH.xlsx',\n",
       " '510290.SH.xlsx',\n",
       " '510300.SH.xlsx',\n",
       " '510310.SH.xlsx',\n",
       " '510330.SH.xlsx',\n",
       " '510350.SH.xlsx',\n",
       " '510360.SH.xlsx',\n",
       " '510370.SH.xlsx',\n",
       " '510380.SH.xlsx',\n",
       " '510390.SH.xlsx',\n",
       " '510410.SH.xlsx',\n",
       " '510500.SH.xlsx',\n",
       " '510510.SH.xlsx',\n",
       " '510530.SH.xlsx',\n",
       " '510550.SH.xlsx',\n",
       " '510560.SH.xlsx',\n",
       " '510570.SH.xlsx',\n",
       " '510580.SH.xlsx',\n",
       " '510590.SH.xlsx',\n",
       " '510600.SH.xlsx',\n",
       " '510630.SH.xlsx',\n",
       " '510650.SH.xlsx',\n",
       " '510660.SH.xlsx',\n",
       " '510680.SH.xlsx',\n",
       " '510710.SH.xlsx',\n",
       " '510760.SH.xlsx',\n",
       " '510770.SH.xlsx',\n",
       " '510800.SH.xlsx',\n",
       " '510810.SH.xlsx',\n",
       " '510850.SH.xlsx',\n",
       " '510880.SH.xlsx',\n",
       " '510900.SH.xlsx',\n",
       " '510990.SH.xlsx',\n",
       " '511010.SH.xlsx',\n",
       " '511020.SH.xlsx',\n",
       " '511030.SH.xlsx',\n",
       " '511060.SH.xlsx',\n",
       " '511090.SH.xlsx',\n",
       " '511180.SH.xlsx',\n",
       " '511220.SH.xlsx',\n",
       " '511260.SH.xlsx',\n",
       " '511270.SH.xlsx',\n",
       " '511360.SH.xlsx',\n",
       " '511380.SH.xlsx',\n",
       " '511520.SH.xlsx',\n",
       " '511580.SH.xlsx',\n",
       " '511600.SH.xlsx',\n",
       " '511620.SH.xlsx',\n",
       " '511650.SH.xlsx',\n",
       " '511660.SH.xlsx',\n",
       " '511670.SH.xlsx',\n",
       " '511690.SH.xlsx',\n",
       " '511700.SH.xlsx',\n",
       " '511770.SH.xlsx',\n",
       " '511800.SH.xlsx',\n",
       " '511810.SH.xlsx',\n",
       " '511820.SH.xlsx',\n",
       " '511830.SH.xlsx',\n",
       " '511850.SH.xlsx',\n",
       " '511860.SH.xlsx',\n",
       " '511880.SH.xlsx',\n",
       " '511900.SH.xlsx',\n",
       " '511910.SH.xlsx',\n",
       " '511920.SH.xlsx',\n",
       " '511930.SH.xlsx',\n",
       " '511950.SH.xlsx',\n",
       " '511960.SH.xlsx',\n",
       " '511970.SH.xlsx',\n",
       " '511980.SH.xlsx',\n",
       " '511990.SH.xlsx',\n",
       " '512000.SH.xlsx',\n",
       " '512010.SH.xlsx',\n",
       " '512040.SH.xlsx',\n",
       " '512070.SH.xlsx',\n",
       " '512090.SH.xlsx',\n",
       " '512100.SH.xlsx',\n",
       " '512120.SH.xlsx',\n",
       " '512150.SH.xlsx',\n",
       " '512160.SH.xlsx',\n",
       " '512170.SH.xlsx',\n",
       " '512180.SH.xlsx',\n",
       " '512190.SH.xlsx',\n",
       " '512200.SH.xlsx',\n",
       " '512220.SH.xlsx',\n",
       " '512260.SH.xlsx',\n",
       " '512280.SH.xlsx',\n",
       " '512290.SH.xlsx',\n",
       " '512320.SH.xlsx',\n",
       " '512330.SH.xlsx',\n",
       " '512360.SH.xlsx',\n",
       " '512380.SH.xlsx',\n",
       " '512390.SH.xlsx',\n",
       " '512400.SH.xlsx',\n",
       " '512480.SH.xlsx',\n",
       " '512500.SH.xlsx',\n",
       " '512510.SH.xlsx',\n",
       " '512520.SH.xlsx',\n",
       " '512530.SH.xlsx',\n",
       " '512550.SH.xlsx',\n",
       " '512560.SH.xlsx',\n",
       " '512570.SH.xlsx',\n",
       " '512580.SH.xlsx',\n",
       " '512600.SH.xlsx',\n",
       " '512640.SH.xlsx',\n",
       " '512650.SH.xlsx',\n",
       " '512660.SH.xlsx',\n",
       " '512670.SH.xlsx',\n",
       " '512680.SH.xlsx',\n",
       " '512690.SH.xlsx',\n",
       " '512700.SH.xlsx',\n",
       " '512710.SH.xlsx',\n",
       " '512720.SH.xlsx',\n",
       " '512730.SH.xlsx',\n",
       " '512750.SH.xlsx',\n",
       " '512760.SH.xlsx',\n",
       " '512770.SH.xlsx',\n",
       " '512800.SH.xlsx',\n",
       " '512810.SH.xlsx',\n",
       " '512820.SH.xlsx',\n",
       " '512870.SH.xlsx',\n",
       " '512880.SH.xlsx',\n",
       " '512890.SH.xlsx',\n",
       " '512900.SH.xlsx',\n",
       " '512910.SH.xlsx',\n",
       " '512930.SH.xlsx',\n",
       " '512950.SH.xlsx',\n",
       " '512960.SH.xlsx',\n",
       " '512970.SH.xlsx',\n",
       " '512980.SH.xlsx',\n",
       " '512990.SH.xlsx',\n",
       " '513000.SH.xlsx',\n",
       " '513010.SH.xlsx',\n",
       " '513020.SH.xlsx',\n",
       " '513030.SH.xlsx',\n",
       " '513040.SH.xlsx',\n",
       " '513050.SH.xlsx',\n",
       " '513060.SH.xlsx',\n",
       " '513070.SH.xlsx',\n",
       " '513080.SH.xlsx',\n",
       " '513090.SH.xlsx',\n",
       " '513100.SH.xlsx',\n",
       " '513110.SH.xlsx',\n",
       " '513120.SH.xlsx',\n",
       " '513130.SH.xlsx',\n",
       " '513140.SH.xlsx',\n",
       " '513150.SH.xlsx',\n",
       " '513160.SH.xlsx',\n",
       " '513180.SH.xlsx',\n",
       " '513190.SH.xlsx',\n",
       " '513193.SH.xlsx',\n",
       " '513200.SH.xlsx',\n",
       " '513220.SH.xlsx',\n",
       " '513230.SH.xlsx',\n",
       " '513260.SH.xlsx',\n",
       " '513280.SH.xlsx',\n",
       " '513290.SH.xlsx',\n",
       " '513300.SH.xlsx',\n",
       " '513310.SH.xlsx',\n",
       " '513320.SH.xlsx',\n",
       " '513330.SH.xlsx',\n",
       " '513360.SH.xlsx',\n",
       " '513380.SH.xlsx',\n",
       " '513390.SH.xlsx',\n",
       " '513500.SH.xlsx',\n",
       " '513520.SH.xlsx',\n",
       " '513530.SH.xlsx',\n",
       " '513550.SH.xlsx',\n",
       " '513560.SH.xlsx',\n",
       " '513580.SH.xlsx',\n",
       " '513590.SH.xlsx',\n",
       " '513600.SH.xlsx',\n",
       " '513650.SH.xlsx',\n",
       " '513660.SH.xlsx',\n",
       " '513690.SH.xlsx',\n",
       " '513700.SH.xlsx',\n",
       " '513770.SH.xlsx',\n",
       " '513800.SH.xlsx',\n",
       " '513810.SH.xlsx',\n",
       " '513853.SH.xlsx',\n",
       " '513854.SH.xlsx',\n",
       " '513860.SH.xlsx',\n",
       " '513873.SH.xlsx',\n",
       " '513880.SH.xlsx',\n",
       " '513890.SH.xlsx',\n",
       " '513900.SH.xlsx',\n",
       " '513950.SH.xlsx',\n",
       " '513960.SH.xlsx',\n",
       " '513970.SH.xlsx',\n",
       " '513980.SH.xlsx',\n",
       " '513990.SH.xlsx',\n",
       " '515000.SH.xlsx',\n",
       " '515010.SH.xlsx',\n",
       " '515020.SH.xlsx',\n",
       " '515030.SH.xlsx',\n",
       " '515050.SH.xlsx',\n",
       " '515060.SH.xlsx',\n",
       " '515070.SH.xlsx',\n",
       " '515080.SH.xlsx',\n",
       " '515090.SH.xlsx',\n",
       " '515100.SH.xlsx',\n",
       " '515110.SH.xlsx',\n",
       " '515120.SH.xlsx',\n",
       " '515130.SH.xlsx',\n",
       " '515150.SH.xlsx',\n",
       " '515160.SH.xlsx',\n",
       " '515170.SH.xlsx',\n",
       " '515180.SH.xlsx',\n",
       " '515190.SH.xlsx',\n",
       " '515200.SH.xlsx',\n",
       " '515210.SH.xlsx',\n",
       " '515220.SH.xlsx',\n",
       " '515230.SH.xlsx',\n",
       " '515250.SH.xlsx',\n",
       " '515260.SH.xlsx',\n",
       " '515280.SH.xlsx',\n",
       " '515290.SH.xlsx',\n",
       " '515300.SH.xlsx',\n",
       " '515310.SH.xlsx',\n",
       " '515320.SH.xlsx',\n",
       " '515330.SH.xlsx',\n",
       " '515350.SH.xlsx',\n",
       " '515360.SH.xlsx',\n",
       " '515380.SH.xlsx',\n",
       " '515390.SH.xlsx',\n",
       " '515400.SH.xlsx',\n",
       " '515450.SH.xlsx',\n",
       " '515530.SH.xlsx',\n",
       " '515550.SH.xlsx',\n",
       " '515560.SH.xlsx',\n",
       " '515570.SH.xlsx',\n",
       " '515580.SH.xlsx',\n",
       " '515590.SH.xlsx',\n",
       " '515600.SH.xlsx',\n",
       " '515630.SH.xlsx',\n",
       " '515650.SH.xlsx',\n",
       " '515660.SH.xlsx',\n",
       " '515670.SH.xlsx',\n",
       " '515680.SH.xlsx',\n",
       " '515700.SH.xlsx',\n",
       " '515710.SH.xlsx',\n",
       " '515750.SH.xlsx',\n",
       " '515760.SH.xlsx',\n",
       " '515770.SH.xlsx',\n",
       " '515780.SH.xlsx',\n",
       " '515790.SH.xlsx',\n",
       " '515800.SH.xlsx',\n",
       " '515810.SH.xlsx',\n",
       " '515850.SH.xlsx',\n",
       " '515860.SH.xlsx',\n",
       " '515880.SH.xlsx',\n",
       " '515890.SH.xlsx',\n",
       " '515900.SH.xlsx',\n",
       " '515910.SH.xlsx',\n",
       " '515920.SH.xlsx',\n",
       " '515950.SH.xlsx',\n",
       " '515960.SH.xlsx',\n",
       " '515980.SH.xlsx',\n",
       " '515990.SH.xlsx',\n",
       " '516000.SH.xlsx',\n",
       " '516010.SH.xlsx',\n",
       " '516020.SH.xlsx',\n",
       " '516050.SH.xlsx',\n",
       " '516060.SH.xlsx',\n",
       " '516070.SH.xlsx',\n",
       " '516080.SH.xlsx',\n",
       " '516090.SH.xlsx',\n",
       " '516100.SH.xlsx',\n",
       " '516110.SH.xlsx',\n",
       " '516120.SH.xlsx',\n",
       " '516130.SH.xlsx',\n",
       " '516150.SH.xlsx',\n",
       " '516160.SH.xlsx',\n",
       " '516180.SH.xlsx',\n",
       " '516190.SH.xlsx',\n",
       " '516200.SH.xlsx',\n",
       " '516210.SH.xlsx',\n",
       " '516220.SH.xlsx',\n",
       " '516260.SH.xlsx',\n",
       " '516270.SH.xlsx',\n",
       " '516290.SH.xlsx',\n",
       " '516300.SH.xlsx',\n",
       " '516310.SH.xlsx',\n",
       " '516320.SH.xlsx',\n",
       " '516330.SH.xlsx',\n",
       " '516350.SH.xlsx',\n",
       " '516360.SH.xlsx',\n",
       " '516380.SH.xlsx',\n",
       " '516390.SH.xlsx',\n",
       " '516480.SH.xlsx',\n",
       " '516500.SH.xlsx',\n",
       " '516510.SH.xlsx',\n",
       " '516520.SH.xlsx',\n",
       " '516530.SH.xlsx',\n",
       " '516550.SH.xlsx',\n",
       " '516560.SH.xlsx',\n",
       " '516570.SH.xlsx',\n",
       " '516580.SH.xlsx',\n",
       " '516590.SH.xlsx',\n",
       " '516600.SH.xlsx',\n",
       " '516610.SH.xlsx',\n",
       " '516620.SH.xlsx',\n",
       " '516630.SH.xlsx',\n",
       " '516640.SH.xlsx',\n",
       " '516650.SH.xlsx',\n",
       " '516660.SH.xlsx',\n",
       " '516670.SH.xlsx',\n",
       " '516690.SH.xlsx',\n",
       " '516700.SH.xlsx',\n",
       " '516710.SH.xlsx',\n",
       " '516720.SH.xlsx',\n",
       " '516730.SH.xlsx',\n",
       " '516750.SH.xlsx',\n",
       " '516760.SH.xlsx',\n",
       " '516770.SH.xlsx',\n",
       " '516780.SH.xlsx',\n",
       " '516790.SH.xlsx',\n",
       " '516800.SH.xlsx',\n",
       " '516810.SH.xlsx',\n",
       " '516820.SH.xlsx',\n",
       " '516830.SH.xlsx',\n",
       " '516850.SH.xlsx',\n",
       " '516860.SH.xlsx',\n",
       " '516880.SH.xlsx',\n",
       " '516890.SH.xlsx',\n",
       " '516900.SH.xlsx',\n",
       " '516910.SH.xlsx',\n",
       " '516920.SH.xlsx',\n",
       " '516930.SH.xlsx',\n",
       " '516950.SH.xlsx',\n",
       " '516960.SH.xlsx',\n",
       " '516970.SH.xlsx',\n",
       " '516980.SH.xlsx',\n",
       " '517000.SH.xlsx',\n",
       " '517010.SH.xlsx',\n",
       " '517030.SH.xlsx',\n",
       " '517050.SH.xlsx',\n",
       " '517080.SH.xlsx',\n",
       " '517090.SH.xlsx',\n",
       " '517100.SH.xlsx',\n",
       " '517110.SH.xlsx',\n",
       " '517120.SH.xlsx',\n",
       " '517160.SH.xlsx',\n",
       " '517170.SH.xlsx',\n",
       " '517180.SH.xlsx',\n",
       " '517200.SH.xlsx',\n",
       " '517270.SH.xlsx',\n",
       " '517280.SH.xlsx',\n",
       " '517300.SH.xlsx',\n",
       " '517330.SH.xlsx',\n",
       " '517350.SH.xlsx',\n",
       " '517360.SH.xlsx',\n",
       " '517380.SH.xlsx',\n",
       " '517390.SH.xlsx',\n",
       " '517500.SH.xlsx',\n",
       " '517523.SH.xlsx',\n",
       " '517550.SH.xlsx',\n",
       " '517660.SH.xlsx',\n",
       " '517760.SH.xlsx',\n",
       " '517770.SH.xlsx',\n",
       " '517780.SH.xlsx',\n",
       " '517800.SH.xlsx',\n",
       " '517850.SH.xlsx',\n",
       " '517880.SH.xlsx',\n",
       " '517900.SH.xlsx',\n",
       " '517960.SH.xlsx',\n",
       " '517990.SH.xlsx',\n",
       " '518600.SH.xlsx',\n",
       " '518660.SH.xlsx',\n",
       " '518680.SH.xlsx',\n",
       " '518800.SH.xlsx',\n",
       " '518850.SH.xlsx',\n",
       " '518860.SH.xlsx',\n",
       " '518880.SH.xlsx',\n",
       " '518890.SH.xlsx',\n",
       " '560000.SH.xlsx',\n",
       " '560010.SH.xlsx',\n",
       " '560020.SH.xlsx',\n",
       " '560023.SH.xlsx',\n",
       " '560030.SH.xlsx',\n",
       " '560050.SH.xlsx',\n",
       " '560060.SH.xlsx',\n",
       " '560070.SH.xlsx',\n",
       " '560080.SH.xlsx',\n",
       " '560090.SH.xlsx',\n",
       " '560100.SH.xlsx',\n",
       " '560110.SH.xlsx',\n",
       " '560170.SH.xlsx',\n",
       " '560180.SH.xlsx',\n",
       " '560220.SH.xlsx',\n",
       " '560223.SH.xlsx',\n",
       " '560260.SH.xlsx',\n",
       " '560280.SH.xlsx',\n",
       " '560283.SH.xlsx',\n",
       " '560330.SH.xlsx',\n",
       " '560500.SH.xlsx',\n",
       " '560550.SH.xlsx',\n",
       " '560560.SH.xlsx',\n",
       " '560580.SH.xlsx',\n",
       " '560590.SH.xlsx',\n",
       " '560600.SH.xlsx',\n",
       " '560650.SH.xlsx',\n",
       " '560660.SH.xlsx',\n",
       " '560680.SH.xlsx',\n",
       " '560700.SH.xlsx',\n",
       " '560800.SH.xlsx',\n",
       " '560860.SH.xlsx',\n",
       " '560880.SH.xlsx',\n",
       " '560900.SH.xlsx',\n",
       " '560950.SH.xlsx',\n",
       " '560960.SH.xlsx',\n",
       " '560980.SH.xlsx',\n",
       " '560990.SH.xlsx',\n",
       " '561000.SH.xlsx',\n",
       " '561060.SH.xlsx',\n",
       " '561100.SH.xlsx',\n",
       " '561120.SH.xlsx',\n",
       " '561130.SH.xlsx',\n",
       " '561160.SH.xlsx',\n",
       " '561170.SH.xlsx',\n",
       " '561180.SH.xlsx',\n",
       " '561190.SH.xlsx',\n",
       " '561260.SH.xlsx',\n",
       " '561280.SH.xlsx',\n",
       " '561300.SH.xlsx',\n",
       " '561310.SH.xlsx',\n",
       " '561320.SH.xlsx',\n",
       " '561330.SH.xlsx',\n",
       " '561350.SH.xlsx',\n",
       " '561360.SH.xlsx',\n",
       " '561363.SH.xlsx',\n",
       " '561370.SH.xlsx',\n",
       " '561373.SH.xlsx',\n",
       " '561500.SH.xlsx',\n",
       " '561510.SH.xlsx',\n",
       " '561550.SH.xlsx',\n",
       " '561560.SH.xlsx',\n",
       " '561580.SH.xlsx',\n",
       " '561590.SH.xlsx',\n",
       " '561600.SH.xlsx',\n",
       " '561700.SH.xlsx',\n",
       " '561710.SH.xlsx',\n",
       " '561783.SH.xlsx',\n",
       " '561784.SH.xlsx',\n",
       " '561790.SH.xlsx',\n",
       " '561800.SH.xlsx',\n",
       " '561900.SH.xlsx',\n",
       " '561910.SH.xlsx',\n",
       " '561920.SH.xlsx',\n",
       " '561950.SH.xlsx',\n",
       " '561960.SH.xlsx',\n",
       " '561980.SH.xlsx',\n",
       " '561990.SH.xlsx',\n",
       " '562000.SH.xlsx',\n",
       " '562010.SH.xlsx',\n",
       " '562030.SH.xlsx',\n",
       " '562033.SH.xlsx',\n",
       " '562300.SH.xlsx',\n",
       " '562310.SH.xlsx',\n",
       " '562320.SH.xlsx',\n",
       " '562330.SH.xlsx',\n",
       " '562350.SH.xlsx',\n",
       " '562360.SH.xlsx',\n",
       " '562380.SH.xlsx',\n",
       " '562390.SH.xlsx',\n",
       " '562500.SH.xlsx',\n",
       " '562510.SH.xlsx',\n",
       " '562520.SH.xlsx',\n",
       " '562530.SH.xlsx',\n",
       " '562550.SH.xlsx',\n",
       " '562590.SH.xlsx',\n",
       " '562593.SH.xlsx',\n",
       " '562660.SH.xlsx',\n",
       " '562663.SH.xlsx',\n",
       " '562800.SH.xlsx',\n",
       " '562850.SH.xlsx',\n",
       " '562860.SH.xlsx',\n",
       " '562880.SH.xlsx',\n",
       " '562900.SH.xlsx',\n",
       " '562910.SH.xlsx',\n",
       " '562920.SH.xlsx',\n",
       " '562930.SH.xlsx',\n",
       " '562950.SH.xlsx',\n",
       " '562960.SH.xlsx',\n",
       " '562990.SH.xlsx',\n",
       " '563000.SH.xlsx',\n",
       " '563010.SH.xlsx',\n",
       " '563030.SH.xlsx',\n",
       " '563050.SH.xlsx',\n",
       " '563200.SH.xlsx',\n",
       " '563203.SH.xlsx',\n",
       " '563300.SH.xlsx',\n",
       " '563303.SH.xlsx',\n",
       " '563333.SH.xlsx',\n",
       " '563334.SH.xlsx',\n",
       " '588000.SH.xlsx',\n",
       " '588010.SH.xlsx',\n",
       " '588020.SH.xlsx',\n",
       " '588030.SH.xlsx',\n",
       " '588050.SH.xlsx',\n",
       " '588060.SH.xlsx',\n",
       " '588080.SH.xlsx',\n",
       " '588090.SH.xlsx',\n",
       " '588100.SH.xlsx',\n",
       " '588110.SH.xlsx',\n",
       " '588120.SH.xlsx',\n",
       " '588150.SH.xlsx',\n",
       " '588160.SH.xlsx',\n",
       " '588180.SH.xlsx',\n",
       " '588190.SH.xlsx',\n",
       " '588200.SH.xlsx',\n",
       " '588213.SH.xlsx',\n",
       " '588214.SH.xlsx',\n",
       " '588220.SH.xlsx',\n",
       " '588260.SH.xlsx',\n",
       " '588280.SH.xlsx',\n",
       " '588290.SH.xlsx',\n",
       " '588300.SH.xlsx',\n",
       " '588310.SH.xlsx',\n",
       " '588320.SH.xlsx',\n",
       " '588330.SH.xlsx',\n",
       " '588350.SH.xlsx',\n",
       " '588360.SH.xlsx',\n",
       " '588370.SH.xlsx',\n",
       " '588380.SH.xlsx',\n",
       " '588390.SH.xlsx',\n",
       " '588400.SH.xlsx',\n",
       " '588460.SH.xlsx']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = r'C:\\Users\\Lenovo\\jupyter_notebook\\金融建模大赛A数据'\n",
    "result = os.listdir(path)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fcd48e81",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_have(name:str):\n",
    "    name = re.findall(\"\\d+\",name)[0]\n",
    "    return name\n",
    "\n",
    "def linear_model(x, a, b):\n",
    "    return a * x + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "end_frame= []\n",
    "for code_index in range(len(result)):\n",
    "    try:\n",
    "        data_fund = pd.read_excel(path+'/'+result[code_index])\n",
    "        up_down = []\n",
    "        code_fund = get_have(result[code_index])\n",
    "        data = ef.fund.get_quote_history(code_fund,pz=75)\n",
    "        # public_dates = ef.fund.get_public_dates(code_fund)\n",
    "        # kk = ef.fund.get_invest_position(code_fund,dates=public_dates[0])\n",
    "\n",
    "        db = ef.stock.get_quote_history(code_fund,beg='20230714',fqt=0)\n",
    "        x = np.array(db['收盘'])\n",
    "        y = np.array(data['单位净值'][::-1])\n",
    "        # 定义线性回归模型函数\n",
    "\n",
    "        # 使用最小二乘法估计参数\n",
    "        params, covariance = curve_fit(linear_model, x, y)\n",
    "        # 提取估计的模型参数\n",
    "        a_est, b_est = params\n",
    "        print('avg_loss:',np.average(linear_model(x, a_est, b_est) - y))\n",
    "        get_like = linear_model(data_fund['收盘价'], a_est, b_est)\n",
    "        # 每只基金的折溢价\n",
    "        for i,j in zip(get_like,data_fund['收盘价']):\n",
    "            cha_values = abs(i-j)\n",
    "            if cha_values > 0:\n",
    "                up_down.append(cha_values)\n",
    "        sum_values = np.sum(up_down)\n",
    "        print(f'{code_fund}',sum_values)\n",
    "        end_frame.append([code_fund,sum_values])\n",
    "    except:\n",
    "        code_fund = get_have(result[code_index])\n",
    "        print('数据异常',code_fund)\n",
    "    print(end_frame)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a0a90b03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      ETF代码         折溢率\n",
      "143  159782    0.159568\n",
      "148  159790    0.244779\n",
      "613  516970    0.297476\n",
      "572  516500    0.358351\n",
      "381  512290    0.420720\n",
      "..      ...         ...\n",
      "355  511520  506.970481\n",
      "353  511360  592.610651\n",
      "492  515180  774.979272\n",
      "351  511260  831.625599\n",
      "528  515800  950.695934\n",
      "\n",
      "[744 rows x 2 columns]\n",
      "            日期    单位净值    累计净值   涨跌幅\n",
      "3   2023-10-31  0.5574  0.5574 -0.52\n",
      "4   2023-10-30  0.5603  0.5603  2.98\n",
      "5   2023-10-27  0.5441  0.5441  2.62\n",
      "6   2023-10-26  0.5302  0.5302 -0.06\n",
      "7   2023-10-25  0.5305  0.5305 -0.82\n",
      "..         ...     ...     ...   ...\n",
      "70  2023-07-20  0.6103  0.6103 -1.01\n",
      "71  2023-07-19  0.6165  0.6165 -0.93\n",
      "72  2023-07-18  0.6223  0.6223 -0.27\n",
      "73  2023-07-17  0.6240  0.6240 -0.68\n",
      "74  2023-07-14  0.6283  0.6283 -0.54\n",
      "\n",
      "[72 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "end_frame_sort = pd.DataFrame(end_frame,columns=['ETF代码','折溢率']).sort_values('折溢率')\n",
    "end_frame_sort = end_frame_sort.drop(end_frame_sort[end_frame_sort['折溢率']>1000].index)\n",
    "print(end_frame_sort)\n",
    "# 获取历史净值\n",
    "code_fund = get_have(result[-3])\n",
    "data = ef.fund.get_quote_history(code_fund,pz=75).iloc[3:,:]\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9c00595a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2023-09-30', '2023-06-30', '2023-03-31', '2022-12-31', '2022-09-30', '2022-06-30', '2022-03-31', '2021-12-31', '2021-08-23']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>基金代码</th>\n",
       "      <th>股票代码</th>\n",
       "      <th>股票简称</th>\n",
       "      <th>持仓占比</th>\n",
       "      <th>较上期变化</th>\n",
       "      <th>公开日期</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>588390</td>\n",
       "      <td>300750</td>\n",
       "      <td>宁德时代</td>\n",
       "      <td>8.87</td>\n",
       "      <td>-1.45</td>\n",
       "      <td>2023-09-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>588390</td>\n",
       "      <td>300760</td>\n",
       "      <td>迈瑞医疗</td>\n",
       "      <td>7.18</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>2023-09-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>588390</td>\n",
       "      <td>300124</td>\n",
       "      <td>汇川技术</td>\n",
       "      <td>6.79</td>\n",
       "      <td>0.70</td>\n",
       "      <td>2023-09-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>588390</td>\n",
       "      <td>688981</td>\n",
       "      <td>中芯国际</td>\n",
       "      <td>5.49</td>\n",
       "      <td>0.46</td>\n",
       "      <td>2023-09-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>588390</td>\n",
       "      <td>300274</td>\n",
       "      <td>阳光电源</td>\n",
       "      <td>5.10</td>\n",
       "      <td>-1.07</td>\n",
       "      <td>2023-09-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>588390</td>\n",
       "      <td>300308</td>\n",
       "      <td>中际旭创</td>\n",
       "      <td>4.08</td>\n",
       "      <td>4.08</td>\n",
       "      <td>2023-09-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>588390</td>\n",
       "      <td>688111</td>\n",
       "      <td>金山办公</td>\n",
       "      <td>3.76</td>\n",
       "      <td>-0.67</td>\n",
       "      <td>2023-09-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>588390</td>\n",
       "      <td>688012</td>\n",
       "      <td>中微公司</td>\n",
       "      <td>3.57</td>\n",
       "      <td>0.12</td>\n",
       "      <td>2023-09-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>588390</td>\n",
       "      <td>300122</td>\n",
       "      <td>智飞生物</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2023-09-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>588390</td>\n",
       "      <td>300014</td>\n",
       "      <td>亿纬锂能</td>\n",
       "      <td>3.04</td>\n",
       "      <td>-0.74</td>\n",
       "      <td>2023-09-30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     基金代码    股票代码  股票简称  持仓占比  较上期变化        公开日期\n",
       "0  588390  300750  宁德时代  8.87  -1.45  2023-09-30\n",
       "1  588390  300760  迈瑞医疗  7.18  -0.21  2023-09-30\n",
       "2  588390  300124  汇川技术  6.79   0.70  2023-09-30\n",
       "3  588390  688981  中芯国际  5.49   0.46  2023-09-30\n",
       "4  588390  300274  阳光电源  5.10  -1.07  2023-09-30\n",
       "5  588390  300308  中际旭创  4.08   4.08  2023-09-30\n",
       "6  588390  688111  金山办公  3.76  -0.67  2023-09-30\n",
       "7  588390  688012  中微公司  3.57   0.12  2023-09-30\n",
       "8  588390  300122  智飞生物  3.20   0.50  2023-09-30\n",
       "9  588390  300014  亿纬锂能  3.04  -0.74  2023-09-30"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "public_dates = ef.fund.get_public_dates(code_fund)\n",
    "print(public_dates)\n",
    "kk = ef.fund.get_invest_position(code_fund,dates=public_dates[0])\n",
    "kk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "7bd40ed2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-11-03\n",
      "2023-11-02\n",
      "2023-11-01\n",
      "2023-10-31\n",
      "2023-10-30\n",
      "2023-10-27\n",
      "2023-10-26\n",
      "2023-10-25\n",
      "2023-10-24\n",
      "2023-10-23\n",
      "2023-10-20\n",
      "2023-10-19\n",
      "2023-10-18\n",
      "2023-10-17\n",
      "2023-10-16\n",
      "2023-10-13\n",
      "2023-10-12\n",
      "2023-10-11\n",
      "2023-10-10\n",
      "2023-10-09\n",
      "2023-09-28\n",
      "2023-09-27\n",
      "2023-09-26\n",
      "2023-09-25\n",
      "2023-09-22\n",
      "2023-09-21\n",
      "2023-09-20\n",
      "2023-09-19\n",
      "2023-09-18\n",
      "2023-09-15\n",
      "2023-09-14\n",
      "2023-09-13\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>...</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.545788</td>\n",
       "      <td>0.557660</td>\n",
       "      <td>0.559901</td>\n",
       "      <td>0.560410</td>\n",
       "      <td>0.536953</td>\n",
       "      <td>0.516980</td>\n",
       "      <td>0.525601</td>\n",
       "      <td>0.544918</td>\n",
       "      <td>0.532418</td>\n",
       "      <td>0.545569</td>\n",
       "      <td>...</td>\n",
       "      <td>0.559139</td>\n",
       "      <td>0.562862</td>\n",
       "      <td>0.566181</td>\n",
       "      <td>0.533214</td>\n",
       "      <td>0.559893</td>\n",
       "      <td>0.561648</td>\n",
       "      <td>0.560371</td>\n",
       "      <td>0.562463</td>\n",
       "      <td>0.562415</td>\n",
       "      <td>0.568874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.546579</td>\n",
       "      <td>0.559481</td>\n",
       "      <td>0.558291</td>\n",
       "      <td>0.556915</td>\n",
       "      <td>0.536704</td>\n",
       "      <td>0.517887</td>\n",
       "      <td>0.526336</td>\n",
       "      <td>0.542150</td>\n",
       "      <td>0.531903</td>\n",
       "      <td>0.542782</td>\n",
       "      <td>...</td>\n",
       "      <td>0.558227</td>\n",
       "      <td>0.563303</td>\n",
       "      <td>0.566003</td>\n",
       "      <td>0.534444</td>\n",
       "      <td>0.560351</td>\n",
       "      <td>0.561581</td>\n",
       "      <td>0.557413</td>\n",
       "      <td>0.566676</td>\n",
       "      <td>0.561532</td>\n",
       "      <td>0.567762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.548533</td>\n",
       "      <td>0.559045</td>\n",
       "      <td>0.558922</td>\n",
       "      <td>0.555957</td>\n",
       "      <td>0.540699</td>\n",
       "      <td>0.520005</td>\n",
       "      <td>0.525601</td>\n",
       "      <td>0.541996</td>\n",
       "      <td>0.530651</td>\n",
       "      <td>0.541626</td>\n",
       "      <td>...</td>\n",
       "      <td>0.559085</td>\n",
       "      <td>0.561701</td>\n",
       "      <td>0.563658</td>\n",
       "      <td>0.535740</td>\n",
       "      <td>0.560702</td>\n",
       "      <td>0.559512</td>\n",
       "      <td>0.558771</td>\n",
       "      <td>0.564838</td>\n",
       "      <td>0.560440</td>\n",
       "      <td>0.569919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.545561</td>\n",
       "      <td>0.558585</td>\n",
       "      <td>0.557666</td>\n",
       "      <td>0.557079</td>\n",
       "      <td>0.541039</td>\n",
       "      <td>0.521909</td>\n",
       "      <td>0.524965</td>\n",
       "      <td>0.542917</td>\n",
       "      <td>0.530220</td>\n",
       "      <td>0.540387</td>\n",
       "      <td>...</td>\n",
       "      <td>0.560940</td>\n",
       "      <td>0.562716</td>\n",
       "      <td>0.565614</td>\n",
       "      <td>0.537939</td>\n",
       "      <td>0.559020</td>\n",
       "      <td>0.559046</td>\n",
       "      <td>0.558019</td>\n",
       "      <td>0.564294</td>\n",
       "      <td>0.561082</td>\n",
       "      <td>0.569098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.547587</td>\n",
       "      <td>0.557536</td>\n",
       "      <td>0.557358</td>\n",
       "      <td>0.559786</td>\n",
       "      <td>0.539940</td>\n",
       "      <td>0.523013</td>\n",
       "      <td>0.522492</td>\n",
       "      <td>0.542500</td>\n",
       "      <td>0.530128</td>\n",
       "      <td>0.538860</td>\n",
       "      <td>...</td>\n",
       "      <td>0.562211</td>\n",
       "      <td>0.561544</td>\n",
       "      <td>0.567048</td>\n",
       "      <td>0.539182</td>\n",
       "      <td>0.558286</td>\n",
       "      <td>0.559681</td>\n",
       "      <td>0.557790</td>\n",
       "      <td>0.561706</td>\n",
       "      <td>0.561812</td>\n",
       "      <td>0.570103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.547452</td>\n",
       "      <td>0.557632</td>\n",
       "      <td>0.557041</td>\n",
       "      <td>0.558394</td>\n",
       "      <td>0.539495</td>\n",
       "      <td>0.520975</td>\n",
       "      <td>0.523450</td>\n",
       "      <td>0.542863</td>\n",
       "      <td>0.528694</td>\n",
       "      <td>0.539164</td>\n",
       "      <td>...</td>\n",
       "      <td>0.563748</td>\n",
       "      <td>0.563432</td>\n",
       "      <td>0.565731</td>\n",
       "      <td>0.539721</td>\n",
       "      <td>0.557488</td>\n",
       "      <td>0.558984</td>\n",
       "      <td>0.557924</td>\n",
       "      <td>0.563692</td>\n",
       "      <td>0.561170</td>\n",
       "      <td>0.568935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.548936</td>\n",
       "      <td>0.556579</td>\n",
       "      <td>0.556605</td>\n",
       "      <td>0.560212</td>\n",
       "      <td>0.541191</td>\n",
       "      <td>0.523487</td>\n",
       "      <td>0.524100</td>\n",
       "      <td>0.540997</td>\n",
       "      <td>0.531663</td>\n",
       "      <td>0.540026</td>\n",
       "      <td>...</td>\n",
       "      <td>0.563025</td>\n",
       "      <td>0.564195</td>\n",
       "      <td>0.564848</td>\n",
       "      <td>0.540014</td>\n",
       "      <td>0.556742</td>\n",
       "      <td>0.559672</td>\n",
       "      <td>0.557665</td>\n",
       "      <td>0.563556</td>\n",
       "      <td>0.565423</td>\n",
       "      <td>0.568568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.549065</td>\n",
       "      <td>0.557004</td>\n",
       "      <td>0.556507</td>\n",
       "      <td>0.560333</td>\n",
       "      <td>0.539953</td>\n",
       "      <td>0.524409</td>\n",
       "      <td>0.524777</td>\n",
       "      <td>0.540982</td>\n",
       "      <td>0.532527</td>\n",
       "      <td>0.541069</td>\n",
       "      <td>...</td>\n",
       "      <td>0.561238</td>\n",
       "      <td>0.564832</td>\n",
       "      <td>0.566200</td>\n",
       "      <td>0.540805</td>\n",
       "      <td>0.556913</td>\n",
       "      <td>0.558741</td>\n",
       "      <td>0.557768</td>\n",
       "      <td>0.566072</td>\n",
       "      <td>0.562943</td>\n",
       "      <td>0.568123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.550449</td>\n",
       "      <td>0.557417</td>\n",
       "      <td>0.557664</td>\n",
       "      <td>0.560191</td>\n",
       "      <td>0.538347</td>\n",
       "      <td>0.523431</td>\n",
       "      <td>0.525806</td>\n",
       "      <td>0.539605</td>\n",
       "      <td>0.533830</td>\n",
       "      <td>0.540687</td>\n",
       "      <td>...</td>\n",
       "      <td>0.560343</td>\n",
       "      <td>0.565645</td>\n",
       "      <td>0.565486</td>\n",
       "      <td>0.542543</td>\n",
       "      <td>0.557606</td>\n",
       "      <td>0.559012</td>\n",
       "      <td>0.558261</td>\n",
       "      <td>0.565603</td>\n",
       "      <td>0.561871</td>\n",
       "      <td>0.566811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.548923</td>\n",
       "      <td>0.556680</td>\n",
       "      <td>0.557301</td>\n",
       "      <td>0.561010</td>\n",
       "      <td>0.538384</td>\n",
       "      <td>0.525303</td>\n",
       "      <td>0.525685</td>\n",
       "      <td>0.539561</td>\n",
       "      <td>0.535434</td>\n",
       "      <td>0.540825</td>\n",
       "      <td>...</td>\n",
       "      <td>0.560809</td>\n",
       "      <td>0.566934</td>\n",
       "      <td>0.565715</td>\n",
       "      <td>0.543959</td>\n",
       "      <td>0.557670</td>\n",
       "      <td>0.558950</td>\n",
       "      <td>0.559371</td>\n",
       "      <td>0.565010</td>\n",
       "      <td>0.560977</td>\n",
       "      <td>0.566489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.549299</td>\n",
       "      <td>0.556085</td>\n",
       "      <td>0.556406</td>\n",
       "      <td>0.560180</td>\n",
       "      <td>0.540432</td>\n",
       "      <td>0.523890</td>\n",
       "      <td>0.524742</td>\n",
       "      <td>0.539281</td>\n",
       "      <td>0.537961</td>\n",
       "      <td>0.540807</td>\n",
       "      <td>...</td>\n",
       "      <td>0.559856</td>\n",
       "      <td>0.565732</td>\n",
       "      <td>0.565627</td>\n",
       "      <td>0.544875</td>\n",
       "      <td>0.557572</td>\n",
       "      <td>0.559567</td>\n",
       "      <td>0.558307</td>\n",
       "      <td>0.566039</td>\n",
       "      <td>0.562747</td>\n",
       "      <td>0.566040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.550436</td>\n",
       "      <td>0.556324</td>\n",
       "      <td>0.556507</td>\n",
       "      <td>0.559699</td>\n",
       "      <td>0.539422</td>\n",
       "      <td>0.523938</td>\n",
       "      <td>0.524262</td>\n",
       "      <td>0.539038</td>\n",
       "      <td>0.536547</td>\n",
       "      <td>0.541973</td>\n",
       "      <td>...</td>\n",
       "      <td>0.560710</td>\n",
       "      <td>0.563686</td>\n",
       "      <td>0.565868</td>\n",
       "      <td>0.544466</td>\n",
       "      <td>0.557453</td>\n",
       "      <td>0.558998</td>\n",
       "      <td>0.559356</td>\n",
       "      <td>0.567064</td>\n",
       "      <td>0.563172</td>\n",
       "      <td>0.567427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.551212</td>\n",
       "      <td>0.556793</td>\n",
       "      <td>0.557277</td>\n",
       "      <td>0.559707</td>\n",
       "      <td>0.540942</td>\n",
       "      <td>0.522979</td>\n",
       "      <td>0.525007</td>\n",
       "      <td>0.538992</td>\n",
       "      <td>0.536010</td>\n",
       "      <td>0.542385</td>\n",
       "      <td>...</td>\n",
       "      <td>0.560493</td>\n",
       "      <td>0.562777</td>\n",
       "      <td>0.565372</td>\n",
       "      <td>0.543432</td>\n",
       "      <td>0.557339</td>\n",
       "      <td>0.559001</td>\n",
       "      <td>0.561077</td>\n",
       "      <td>0.565854</td>\n",
       "      <td>0.563044</td>\n",
       "      <td>0.567474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.552922</td>\n",
       "      <td>0.556820</td>\n",
       "      <td>0.557796</td>\n",
       "      <td>0.559997</td>\n",
       "      <td>0.540404</td>\n",
       "      <td>0.522134</td>\n",
       "      <td>0.524338</td>\n",
       "      <td>0.538102</td>\n",
       "      <td>0.536266</td>\n",
       "      <td>0.541978</td>\n",
       "      <td>...</td>\n",
       "      <td>0.559644</td>\n",
       "      <td>0.562736</td>\n",
       "      <td>0.565987</td>\n",
       "      <td>0.542271</td>\n",
       "      <td>0.557339</td>\n",
       "      <td>0.558947</td>\n",
       "      <td>0.561172</td>\n",
       "      <td>0.567269</td>\n",
       "      <td>0.562034</td>\n",
       "      <td>0.569174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.553334</td>\n",
       "      <td>0.558156</td>\n",
       "      <td>0.558656</td>\n",
       "      <td>0.559608</td>\n",
       "      <td>0.540062</td>\n",
       "      <td>0.521968</td>\n",
       "      <td>0.524669</td>\n",
       "      <td>0.537730</td>\n",
       "      <td>0.536577</td>\n",
       "      <td>0.542592</td>\n",
       "      <td>...</td>\n",
       "      <td>0.559656</td>\n",
       "      <td>0.563863</td>\n",
       "      <td>0.565787</td>\n",
       "      <td>0.542055</td>\n",
       "      <td>0.557633</td>\n",
       "      <td>0.559404</td>\n",
       "      <td>0.563250</td>\n",
       "      <td>0.565443</td>\n",
       "      <td>0.563599</td>\n",
       "      <td>0.568217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.552497</td>\n",
       "      <td>0.557912</td>\n",
       "      <td>0.559137</td>\n",
       "      <td>0.558997</td>\n",
       "      <td>0.541248</td>\n",
       "      <td>0.523024</td>\n",
       "      <td>0.524244</td>\n",
       "      <td>0.538479</td>\n",
       "      <td>0.535246</td>\n",
       "      <td>0.542235</td>\n",
       "      <td>...</td>\n",
       "      <td>0.559100</td>\n",
       "      <td>0.563910</td>\n",
       "      <td>0.565111</td>\n",
       "      <td>0.542146</td>\n",
       "      <td>0.557301</td>\n",
       "      <td>0.558942</td>\n",
       "      <td>0.563535</td>\n",
       "      <td>0.564344</td>\n",
       "      <td>0.564728</td>\n",
       "      <td>0.567956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.551989</td>\n",
       "      <td>0.558972</td>\n",
       "      <td>0.559606</td>\n",
       "      <td>0.558510</td>\n",
       "      <td>0.539711</td>\n",
       "      <td>0.521373</td>\n",
       "      <td>0.524296</td>\n",
       "      <td>0.539035</td>\n",
       "      <td>0.534749</td>\n",
       "      <td>0.542595</td>\n",
       "      <td>...</td>\n",
       "      <td>0.558679</td>\n",
       "      <td>0.563362</td>\n",
       "      <td>0.565695</td>\n",
       "      <td>0.543366</td>\n",
       "      <td>0.557108</td>\n",
       "      <td>0.558663</td>\n",
       "      <td>0.561913</td>\n",
       "      <td>0.563932</td>\n",
       "      <td>0.566303</td>\n",
       "      <td>0.567791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.551585</td>\n",
       "      <td>0.556841</td>\n",
       "      <td>0.561160</td>\n",
       "      <td>0.558562</td>\n",
       "      <td>0.540462</td>\n",
       "      <td>0.521800</td>\n",
       "      <td>0.523479</td>\n",
       "      <td>0.538552</td>\n",
       "      <td>0.534006</td>\n",
       "      <td>0.541979</td>\n",
       "      <td>...</td>\n",
       "      <td>0.558655</td>\n",
       "      <td>0.562620</td>\n",
       "      <td>0.565537</td>\n",
       "      <td>0.544085</td>\n",
       "      <td>0.557224</td>\n",
       "      <td>0.558933</td>\n",
       "      <td>0.562975</td>\n",
       "      <td>0.563955</td>\n",
       "      <td>0.566034</td>\n",
       "      <td>0.567380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.550623</td>\n",
       "      <td>0.557068</td>\n",
       "      <td>0.559656</td>\n",
       "      <td>0.559370</td>\n",
       "      <td>0.540657</td>\n",
       "      <td>0.521152</td>\n",
       "      <td>0.522942</td>\n",
       "      <td>0.538266</td>\n",
       "      <td>0.534089</td>\n",
       "      <td>0.541570</td>\n",
       "      <td>...</td>\n",
       "      <td>0.557948</td>\n",
       "      <td>0.562113</td>\n",
       "      <td>0.565035</td>\n",
       "      <td>0.544619</td>\n",
       "      <td>0.557614</td>\n",
       "      <td>0.558582</td>\n",
       "      <td>0.562824</td>\n",
       "      <td>0.563642</td>\n",
       "      <td>0.565040</td>\n",
       "      <td>0.568044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.549896</td>\n",
       "      <td>0.556368</td>\n",
       "      <td>0.559461</td>\n",
       "      <td>0.558722</td>\n",
       "      <td>0.541079</td>\n",
       "      <td>0.519663</td>\n",
       "      <td>0.522866</td>\n",
       "      <td>0.537573</td>\n",
       "      <td>0.533761</td>\n",
       "      <td>0.542021</td>\n",
       "      <td>...</td>\n",
       "      <td>0.557837</td>\n",
       "      <td>0.562455</td>\n",
       "      <td>0.564521</td>\n",
       "      <td>0.543908</td>\n",
       "      <td>0.557389</td>\n",
       "      <td>0.558317</td>\n",
       "      <td>0.562765</td>\n",
       "      <td>0.563222</td>\n",
       "      <td>0.564654</td>\n",
       "      <td>0.567993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.549610</td>\n",
       "      <td>0.556450</td>\n",
       "      <td>0.559422</td>\n",
       "      <td>0.558953</td>\n",
       "      <td>0.541672</td>\n",
       "      <td>0.520356</td>\n",
       "      <td>0.522483</td>\n",
       "      <td>0.536976</td>\n",
       "      <td>0.532782</td>\n",
       "      <td>0.541775</td>\n",
       "      <td>...</td>\n",
       "      <td>0.558044</td>\n",
       "      <td>0.562873</td>\n",
       "      <td>0.564368</td>\n",
       "      <td>0.544395</td>\n",
       "      <td>0.557230</td>\n",
       "      <td>0.558579</td>\n",
       "      <td>0.562706</td>\n",
       "      <td>0.564368</td>\n",
       "      <td>0.564160</td>\n",
       "      <td>0.567490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.550663</td>\n",
       "      <td>0.556220</td>\n",
       "      <td>0.559213</td>\n",
       "      <td>0.558285</td>\n",
       "      <td>0.542592</td>\n",
       "      <td>0.519496</td>\n",
       "      <td>0.522479</td>\n",
       "      <td>0.536723</td>\n",
       "      <td>0.532590</td>\n",
       "      <td>0.541605</td>\n",
       "      <td>...</td>\n",
       "      <td>0.558666</td>\n",
       "      <td>0.562383</td>\n",
       "      <td>0.564101</td>\n",
       "      <td>0.544942</td>\n",
       "      <td>0.556924</td>\n",
       "      <td>0.559004</td>\n",
       "      <td>0.563269</td>\n",
       "      <td>0.563505</td>\n",
       "      <td>0.565265</td>\n",
       "      <td>0.567519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.551087</td>\n",
       "      <td>0.556402</td>\n",
       "      <td>0.560224</td>\n",
       "      <td>0.558784</td>\n",
       "      <td>0.544490</td>\n",
       "      <td>0.520416</td>\n",
       "      <td>0.523082</td>\n",
       "      <td>0.536235</td>\n",
       "      <td>0.532949</td>\n",
       "      <td>0.541562</td>\n",
       "      <td>...</td>\n",
       "      <td>0.558682</td>\n",
       "      <td>0.562637</td>\n",
       "      <td>0.565331</td>\n",
       "      <td>0.545559</td>\n",
       "      <td>0.557283</td>\n",
       "      <td>0.558864</td>\n",
       "      <td>0.562871</td>\n",
       "      <td>0.563499</td>\n",
       "      <td>0.565349</td>\n",
       "      <td>0.568001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.550764</td>\n",
       "      <td>0.556363</td>\n",
       "      <td>0.558600</td>\n",
       "      <td>0.558623</td>\n",
       "      <td>0.545707</td>\n",
       "      <td>0.520101</td>\n",
       "      <td>0.523199</td>\n",
       "      <td>0.536379</td>\n",
       "      <td>0.532466</td>\n",
       "      <td>0.542206</td>\n",
       "      <td>...</td>\n",
       "      <td>0.559271</td>\n",
       "      <td>0.562748</td>\n",
       "      <td>0.565249</td>\n",
       "      <td>0.545468</td>\n",
       "      <td>0.557060</td>\n",
       "      <td>0.558909</td>\n",
       "      <td>0.561764</td>\n",
       "      <td>0.562941</td>\n",
       "      <td>0.565287</td>\n",
       "      <td>0.568623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.550720</td>\n",
       "      <td>0.556603</td>\n",
       "      <td>0.558423</td>\n",
       "      <td>0.557452</td>\n",
       "      <td>0.545859</td>\n",
       "      <td>0.520838</td>\n",
       "      <td>0.521764</td>\n",
       "      <td>0.536137</td>\n",
       "      <td>0.532056</td>\n",
       "      <td>0.541930</td>\n",
       "      <td>...</td>\n",
       "      <td>0.557741</td>\n",
       "      <td>0.562583</td>\n",
       "      <td>0.564636</td>\n",
       "      <td>0.544568</td>\n",
       "      <td>0.556935</td>\n",
       "      <td>0.559008</td>\n",
       "      <td>0.560260</td>\n",
       "      <td>0.562051</td>\n",
       "      <td>0.563803</td>\n",
       "      <td>0.568173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.550924</td>\n",
       "      <td>0.555803</td>\n",
       "      <td>0.558202</td>\n",
       "      <td>0.558152</td>\n",
       "      <td>0.544787</td>\n",
       "      <td>0.520536</td>\n",
       "      <td>0.521517</td>\n",
       "      <td>0.536185</td>\n",
       "      <td>0.531933</td>\n",
       "      <td>0.542049</td>\n",
       "      <td>...</td>\n",
       "      <td>0.558180</td>\n",
       "      <td>0.561855</td>\n",
       "      <td>0.564653</td>\n",
       "      <td>0.545977</td>\n",
       "      <td>0.556881</td>\n",
       "      <td>0.559449</td>\n",
       "      <td>0.560214</td>\n",
       "      <td>0.561728</td>\n",
       "      <td>0.563026</td>\n",
       "      <td>0.567816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.550195</td>\n",
       "      <td>0.556447</td>\n",
       "      <td>0.558153</td>\n",
       "      <td>0.557863</td>\n",
       "      <td>0.543014</td>\n",
       "      <td>0.522060</td>\n",
       "      <td>0.521085</td>\n",
       "      <td>0.536445</td>\n",
       "      <td>0.531684</td>\n",
       "      <td>0.541927</td>\n",
       "      <td>...</td>\n",
       "      <td>0.558658</td>\n",
       "      <td>0.562378</td>\n",
       "      <td>0.564587</td>\n",
       "      <td>0.545364</td>\n",
       "      <td>0.557535</td>\n",
       "      <td>0.559116</td>\n",
       "      <td>0.560769</td>\n",
       "      <td>0.561344</td>\n",
       "      <td>0.562247</td>\n",
       "      <td>0.567339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.550313</td>\n",
       "      <td>0.557244</td>\n",
       "      <td>0.558396</td>\n",
       "      <td>0.557642</td>\n",
       "      <td>0.544113</td>\n",
       "      <td>0.523001</td>\n",
       "      <td>0.521308</td>\n",
       "      <td>0.535519</td>\n",
       "      <td>0.530989</td>\n",
       "      <td>0.541983</td>\n",
       "      <td>...</td>\n",
       "      <td>0.557250</td>\n",
       "      <td>0.562720</td>\n",
       "      <td>0.564767</td>\n",
       "      <td>0.544535</td>\n",
       "      <td>0.556192</td>\n",
       "      <td>0.559385</td>\n",
       "      <td>0.561174</td>\n",
       "      <td>0.560838</td>\n",
       "      <td>0.561663</td>\n",
       "      <td>0.566780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.550290</td>\n",
       "      <td>0.556717</td>\n",
       "      <td>0.558378</td>\n",
       "      <td>0.558235</td>\n",
       "      <td>0.543867</td>\n",
       "      <td>0.523229</td>\n",
       "      <td>0.520858</td>\n",
       "      <td>0.535890</td>\n",
       "      <td>0.530931</td>\n",
       "      <td>0.541601</td>\n",
       "      <td>...</td>\n",
       "      <td>0.557437</td>\n",
       "      <td>0.562836</td>\n",
       "      <td>0.565326</td>\n",
       "      <td>0.544206</td>\n",
       "      <td>0.555699</td>\n",
       "      <td>0.558922</td>\n",
       "      <td>0.561047</td>\n",
       "      <td>0.560621</td>\n",
       "      <td>0.561429</td>\n",
       "      <td>0.566543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.550638</td>\n",
       "      <td>0.556498</td>\n",
       "      <td>0.558156</td>\n",
       "      <td>0.557446</td>\n",
       "      <td>0.544080</td>\n",
       "      <td>0.523748</td>\n",
       "      <td>0.520553</td>\n",
       "      <td>0.535709</td>\n",
       "      <td>0.529990</td>\n",
       "      <td>0.541232</td>\n",
       "      <td>...</td>\n",
       "      <td>0.557901</td>\n",
       "      <td>0.563163</td>\n",
       "      <td>0.565048</td>\n",
       "      <td>0.544340</td>\n",
       "      <td>0.555428</td>\n",
       "      <td>0.559219</td>\n",
       "      <td>0.560898</td>\n",
       "      <td>0.560975</td>\n",
       "      <td>0.561434</td>\n",
       "      <td>0.567200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.550832</td>\n",
       "      <td>0.556708</td>\n",
       "      <td>0.557959</td>\n",
       "      <td>0.558358</td>\n",
       "      <td>0.544307</td>\n",
       "      <td>0.523530</td>\n",
       "      <td>0.520540</td>\n",
       "      <td>0.534904</td>\n",
       "      <td>0.530225</td>\n",
       "      <td>0.541799</td>\n",
       "      <td>...</td>\n",
       "      <td>0.558380</td>\n",
       "      <td>0.562917</td>\n",
       "      <td>0.565170</td>\n",
       "      <td>0.544537</td>\n",
       "      <td>0.555706</td>\n",
       "      <td>0.559684</td>\n",
       "      <td>0.562171</td>\n",
       "      <td>0.560663</td>\n",
       "      <td>0.561089</td>\n",
       "      <td>0.567007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.551206</td>\n",
       "      <td>0.556002</td>\n",
       "      <td>0.557540</td>\n",
       "      <td>0.558551</td>\n",
       "      <td>0.545109</td>\n",
       "      <td>0.524101</td>\n",
       "      <td>0.521077</td>\n",
       "      <td>0.535283</td>\n",
       "      <td>0.529857</td>\n",
       "      <td>0.542656</td>\n",
       "      <td>...</td>\n",
       "      <td>0.558490</td>\n",
       "      <td>0.563059</td>\n",
       "      <td>0.564879</td>\n",
       "      <td>0.544445</td>\n",
       "      <td>0.555855</td>\n",
       "      <td>0.560518</td>\n",
       "      <td>0.561699</td>\n",
       "      <td>0.560779</td>\n",
       "      <td>0.560544</td>\n",
       "      <td>0.567084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.551074</td>\n",
       "      <td>0.556671</td>\n",
       "      <td>0.557550</td>\n",
       "      <td>0.559177</td>\n",
       "      <td>0.544476</td>\n",
       "      <td>0.527211</td>\n",
       "      <td>0.522015</td>\n",
       "      <td>0.535856</td>\n",
       "      <td>0.529804</td>\n",
       "      <td>0.542008</td>\n",
       "      <td>...</td>\n",
       "      <td>0.559286</td>\n",
       "      <td>0.562881</td>\n",
       "      <td>0.564713</td>\n",
       "      <td>0.543821</td>\n",
       "      <td>0.555143</td>\n",
       "      <td>0.560097</td>\n",
       "      <td>0.561815</td>\n",
       "      <td>0.561285</td>\n",
       "      <td>0.560877</td>\n",
       "      <td>0.566475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.550571</td>\n",
       "      <td>0.556271</td>\n",
       "      <td>0.557768</td>\n",
       "      <td>0.558436</td>\n",
       "      <td>0.544604</td>\n",
       "      <td>0.527461</td>\n",
       "      <td>0.522776</td>\n",
       "      <td>0.536072</td>\n",
       "      <td>0.530395</td>\n",
       "      <td>0.541624</td>\n",
       "      <td>...</td>\n",
       "      <td>0.558712</td>\n",
       "      <td>0.562352</td>\n",
       "      <td>0.564654</td>\n",
       "      <td>0.543640</td>\n",
       "      <td>0.555006</td>\n",
       "      <td>0.560146</td>\n",
       "      <td>0.561855</td>\n",
       "      <td>0.561331</td>\n",
       "      <td>0.560786</td>\n",
       "      <td>0.565807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.550477</td>\n",
       "      <td>0.556096</td>\n",
       "      <td>0.558346</td>\n",
       "      <td>0.558920</td>\n",
       "      <td>0.544467</td>\n",
       "      <td>0.527710</td>\n",
       "      <td>0.523120</td>\n",
       "      <td>0.535673</td>\n",
       "      <td>0.531398</td>\n",
       "      <td>0.541741</td>\n",
       "      <td>...</td>\n",
       "      <td>0.559140</td>\n",
       "      <td>0.561684</td>\n",
       "      <td>0.564264</td>\n",
       "      <td>0.544189</td>\n",
       "      <td>0.555086</td>\n",
       "      <td>0.559272</td>\n",
       "      <td>0.561781</td>\n",
       "      <td>0.561284</td>\n",
       "      <td>0.560327</td>\n",
       "      <td>0.565160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.550346</td>\n",
       "      <td>0.555028</td>\n",
       "      <td>0.558569</td>\n",
       "      <td>0.558864</td>\n",
       "      <td>0.544834</td>\n",
       "      <td>0.528560</td>\n",
       "      <td>0.522788</td>\n",
       "      <td>0.536719</td>\n",
       "      <td>0.531064</td>\n",
       "      <td>0.541214</td>\n",
       "      <td>...</td>\n",
       "      <td>0.558707</td>\n",
       "      <td>0.561510</td>\n",
       "      <td>0.563619</td>\n",
       "      <td>0.545624</td>\n",
       "      <td>0.555001</td>\n",
       "      <td>0.559188</td>\n",
       "      <td>0.561637</td>\n",
       "      <td>0.560099</td>\n",
       "      <td>0.560435</td>\n",
       "      <td>0.564524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.550358</td>\n",
       "      <td>0.555318</td>\n",
       "      <td>0.558822</td>\n",
       "      <td>0.558824</td>\n",
       "      <td>0.545432</td>\n",
       "      <td>0.529000</td>\n",
       "      <td>0.522040</td>\n",
       "      <td>0.536369</td>\n",
       "      <td>0.532591</td>\n",
       "      <td>0.540442</td>\n",
       "      <td>...</td>\n",
       "      <td>0.558695</td>\n",
       "      <td>0.561139</td>\n",
       "      <td>0.562779</td>\n",
       "      <td>0.547683</td>\n",
       "      <td>0.555676</td>\n",
       "      <td>0.559275</td>\n",
       "      <td>0.560924</td>\n",
       "      <td>0.560814</td>\n",
       "      <td>0.561391</td>\n",
       "      <td>0.564584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.550831</td>\n",
       "      <td>0.554436</td>\n",
       "      <td>0.559175</td>\n",
       "      <td>0.558660</td>\n",
       "      <td>0.544944</td>\n",
       "      <td>0.530481</td>\n",
       "      <td>0.523253</td>\n",
       "      <td>0.536591</td>\n",
       "      <td>0.532295</td>\n",
       "      <td>0.541021</td>\n",
       "      <td>...</td>\n",
       "      <td>0.560097</td>\n",
       "      <td>0.561498</td>\n",
       "      <td>0.563312</td>\n",
       "      <td>0.548987</td>\n",
       "      <td>0.555925</td>\n",
       "      <td>0.559366</td>\n",
       "      <td>0.561664</td>\n",
       "      <td>0.561785</td>\n",
       "      <td>0.560882</td>\n",
       "      <td>0.564934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.550477</td>\n",
       "      <td>0.554257</td>\n",
       "      <td>0.559695</td>\n",
       "      <td>0.558686</td>\n",
       "      <td>0.545223</td>\n",
       "      <td>0.529877</td>\n",
       "      <td>0.524979</td>\n",
       "      <td>0.535902</td>\n",
       "      <td>0.532274</td>\n",
       "      <td>0.540298</td>\n",
       "      <td>...</td>\n",
       "      <td>0.559908</td>\n",
       "      <td>0.561370</td>\n",
       "      <td>0.563463</td>\n",
       "      <td>0.549524</td>\n",
       "      <td>0.557236</td>\n",
       "      <td>0.559984</td>\n",
       "      <td>0.561392</td>\n",
       "      <td>0.560708</td>\n",
       "      <td>0.561262</td>\n",
       "      <td>0.564667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.550052</td>\n",
       "      <td>0.555326</td>\n",
       "      <td>0.559391</td>\n",
       "      <td>0.558846</td>\n",
       "      <td>0.545710</td>\n",
       "      <td>0.529863</td>\n",
       "      <td>0.527601</td>\n",
       "      <td>0.536555</td>\n",
       "      <td>0.531479</td>\n",
       "      <td>0.539844</td>\n",
       "      <td>...</td>\n",
       "      <td>0.559289</td>\n",
       "      <td>0.561280</td>\n",
       "      <td>0.562858</td>\n",
       "      <td>0.549775</td>\n",
       "      <td>0.556167</td>\n",
       "      <td>0.559655</td>\n",
       "      <td>0.561764</td>\n",
       "      <td>0.560796</td>\n",
       "      <td>0.560954</td>\n",
       "      <td>0.565410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.549518</td>\n",
       "      <td>0.554997</td>\n",
       "      <td>0.558406</td>\n",
       "      <td>0.558907</td>\n",
       "      <td>0.546154</td>\n",
       "      <td>0.529732</td>\n",
       "      <td>0.527177</td>\n",
       "      <td>0.536593</td>\n",
       "      <td>0.532398</td>\n",
       "      <td>0.539048</td>\n",
       "      <td>...</td>\n",
       "      <td>0.558897</td>\n",
       "      <td>0.561975</td>\n",
       "      <td>0.562833</td>\n",
       "      <td>0.550548</td>\n",
       "      <td>0.556228</td>\n",
       "      <td>0.560308</td>\n",
       "      <td>0.561763</td>\n",
       "      <td>0.561381</td>\n",
       "      <td>0.560710</td>\n",
       "      <td>0.565127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.549835</td>\n",
       "      <td>0.554926</td>\n",
       "      <td>0.558164</td>\n",
       "      <td>0.559831</td>\n",
       "      <td>0.545960</td>\n",
       "      <td>0.530565</td>\n",
       "      <td>0.527213</td>\n",
       "      <td>0.536433</td>\n",
       "      <td>0.533583</td>\n",
       "      <td>0.538532</td>\n",
       "      <td>...</td>\n",
       "      <td>0.558934</td>\n",
       "      <td>0.561842</td>\n",
       "      <td>0.563104</td>\n",
       "      <td>0.549809</td>\n",
       "      <td>0.555543</td>\n",
       "      <td>0.559797</td>\n",
       "      <td>0.561787</td>\n",
       "      <td>0.561190</td>\n",
       "      <td>0.560387</td>\n",
       "      <td>0.565715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.549775</td>\n",
       "      <td>0.554893</td>\n",
       "      <td>0.557636</td>\n",
       "      <td>0.560125</td>\n",
       "      <td>0.545169</td>\n",
       "      <td>0.529683</td>\n",
       "      <td>0.528194</td>\n",
       "      <td>0.535575</td>\n",
       "      <td>0.532285</td>\n",
       "      <td>0.539913</td>\n",
       "      <td>...</td>\n",
       "      <td>0.558865</td>\n",
       "      <td>0.560843</td>\n",
       "      <td>0.562838</td>\n",
       "      <td>0.550838</td>\n",
       "      <td>0.554657</td>\n",
       "      <td>0.559401</td>\n",
       "      <td>0.561978</td>\n",
       "      <td>0.560715</td>\n",
       "      <td>0.561477</td>\n",
       "      <td>0.567075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.550139</td>\n",
       "      <td>0.554451</td>\n",
       "      <td>0.557780</td>\n",
       "      <td>0.561023</td>\n",
       "      <td>0.545278</td>\n",
       "      <td>0.529981</td>\n",
       "      <td>0.529921</td>\n",
       "      <td>0.535989</td>\n",
       "      <td>0.532386</td>\n",
       "      <td>0.540099</td>\n",
       "      <td>...</td>\n",
       "      <td>0.559340</td>\n",
       "      <td>0.561211</td>\n",
       "      <td>0.563181</td>\n",
       "      <td>0.550607</td>\n",
       "      <td>0.554796</td>\n",
       "      <td>0.559493</td>\n",
       "      <td>0.562094</td>\n",
       "      <td>0.560651</td>\n",
       "      <td>0.562700</td>\n",
       "      <td>0.566993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.550489</td>\n",
       "      <td>0.554196</td>\n",
       "      <td>0.557850</td>\n",
       "      <td>0.561947</td>\n",
       "      <td>0.544759</td>\n",
       "      <td>0.530427</td>\n",
       "      <td>0.530030</td>\n",
       "      <td>0.535728</td>\n",
       "      <td>0.532907</td>\n",
       "      <td>0.539244</td>\n",
       "      <td>...</td>\n",
       "      <td>0.559143</td>\n",
       "      <td>0.561454</td>\n",
       "      <td>0.562820</td>\n",
       "      <td>0.550293</td>\n",
       "      <td>0.554740</td>\n",
       "      <td>0.559041</td>\n",
       "      <td>0.562318</td>\n",
       "      <td>0.560624</td>\n",
       "      <td>0.561902</td>\n",
       "      <td>0.566917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.550381</td>\n",
       "      <td>0.553776</td>\n",
       "      <td>0.557622</td>\n",
       "      <td>0.561816</td>\n",
       "      <td>0.544953</td>\n",
       "      <td>0.530094</td>\n",
       "      <td>0.530487</td>\n",
       "      <td>0.535418</td>\n",
       "      <td>0.532935</td>\n",
       "      <td>0.540304</td>\n",
       "      <td>...</td>\n",
       "      <td>0.559027</td>\n",
       "      <td>0.561146</td>\n",
       "      <td>0.562837</td>\n",
       "      <td>0.549990</td>\n",
       "      <td>0.554869</td>\n",
       "      <td>0.558475</td>\n",
       "      <td>0.561720</td>\n",
       "      <td>0.560480</td>\n",
       "      <td>0.561805</td>\n",
       "      <td>0.566409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.550014</td>\n",
       "      <td>0.553515</td>\n",
       "      <td>0.557524</td>\n",
       "      <td>0.561600</td>\n",
       "      <td>0.544135</td>\n",
       "      <td>0.529829</td>\n",
       "      <td>0.530489</td>\n",
       "      <td>0.535061</td>\n",
       "      <td>0.532965</td>\n",
       "      <td>0.540850</td>\n",
       "      <td>...</td>\n",
       "      <td>0.558711</td>\n",
       "      <td>0.560766</td>\n",
       "      <td>0.562524</td>\n",
       "      <td>0.549748</td>\n",
       "      <td>0.554676</td>\n",
       "      <td>0.558111</td>\n",
       "      <td>0.561438</td>\n",
       "      <td>0.560766</td>\n",
       "      <td>0.561847</td>\n",
       "      <td>0.566404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.553200</td>\n",
       "      <td>0.557400</td>\n",
       "      <td>0.560300</td>\n",
       "      <td>0.544100</td>\n",
       "      <td>0.530200</td>\n",
       "      <td>0.530500</td>\n",
       "      <td>0.534900</td>\n",
       "      <td>0.532300</td>\n",
       "      <td>0.541400</td>\n",
       "      <td>...</td>\n",
       "      <td>0.558300</td>\n",
       "      <td>0.560500</td>\n",
       "      <td>0.563000</td>\n",
       "      <td>0.550200</td>\n",
       "      <td>0.554400</td>\n",
       "      <td>0.558400</td>\n",
       "      <td>0.561800</td>\n",
       "      <td>0.561600</td>\n",
       "      <td>0.561400</td>\n",
       "      <td>0.566600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         0         0         0         0         0         0  \\\n",
       "0   0.545788  0.557660  0.559901  0.560410  0.536953  0.516980  0.525601   \n",
       "1   0.546579  0.559481  0.558291  0.556915  0.536704  0.517887  0.526336   \n",
       "2   0.548533  0.559045  0.558922  0.555957  0.540699  0.520005  0.525601   \n",
       "3   0.545561  0.558585  0.557666  0.557079  0.541039  0.521909  0.524965   \n",
       "4   0.547587  0.557536  0.557358  0.559786  0.539940  0.523013  0.522492   \n",
       "5   0.547452  0.557632  0.557041  0.558394  0.539495  0.520975  0.523450   \n",
       "6   0.548936  0.556579  0.556605  0.560212  0.541191  0.523487  0.524100   \n",
       "7   0.549065  0.557004  0.556507  0.560333  0.539953  0.524409  0.524777   \n",
       "8   0.550449  0.557417  0.557664  0.560191  0.538347  0.523431  0.525806   \n",
       "9   0.548923  0.556680  0.557301  0.561010  0.538384  0.525303  0.525685   \n",
       "10  0.549299  0.556085  0.556406  0.560180  0.540432  0.523890  0.524742   \n",
       "11  0.550436  0.556324  0.556507  0.559699  0.539422  0.523938  0.524262   \n",
       "12  0.551212  0.556793  0.557277  0.559707  0.540942  0.522979  0.525007   \n",
       "13  0.552922  0.556820  0.557796  0.559997  0.540404  0.522134  0.524338   \n",
       "14  0.553334  0.558156  0.558656  0.559608  0.540062  0.521968  0.524669   \n",
       "15  0.552497  0.557912  0.559137  0.558997  0.541248  0.523024  0.524244   \n",
       "16  0.551989  0.558972  0.559606  0.558510  0.539711  0.521373  0.524296   \n",
       "17  0.551585  0.556841  0.561160  0.558562  0.540462  0.521800  0.523479   \n",
       "18  0.550623  0.557068  0.559656  0.559370  0.540657  0.521152  0.522942   \n",
       "19  0.549896  0.556368  0.559461  0.558722  0.541079  0.519663  0.522866   \n",
       "20  0.549610  0.556450  0.559422  0.558953  0.541672  0.520356  0.522483   \n",
       "21  0.550663  0.556220  0.559213  0.558285  0.542592  0.519496  0.522479   \n",
       "22  0.551087  0.556402  0.560224  0.558784  0.544490  0.520416  0.523082   \n",
       "23  0.550764  0.556363  0.558600  0.558623  0.545707  0.520101  0.523199   \n",
       "24  0.550720  0.556603  0.558423  0.557452  0.545859  0.520838  0.521764   \n",
       "25  0.550924  0.555803  0.558202  0.558152  0.544787  0.520536  0.521517   \n",
       "26  0.550195  0.556447  0.558153  0.557863  0.543014  0.522060  0.521085   \n",
       "27  0.550313  0.557244  0.558396  0.557642  0.544113  0.523001  0.521308   \n",
       "28  0.550290  0.556717  0.558378  0.558235  0.543867  0.523229  0.520858   \n",
       "29  0.550638  0.556498  0.558156  0.557446  0.544080  0.523748  0.520553   \n",
       "30  0.550832  0.556708  0.557959  0.558358  0.544307  0.523530  0.520540   \n",
       "31  0.551206  0.556002  0.557540  0.558551  0.545109  0.524101  0.521077   \n",
       "32  0.551074  0.556671  0.557550  0.559177  0.544476  0.527211  0.522015   \n",
       "33  0.550571  0.556271  0.557768  0.558436  0.544604  0.527461  0.522776   \n",
       "34  0.550477  0.556096  0.558346  0.558920  0.544467  0.527710  0.523120   \n",
       "35  0.550346  0.555028  0.558569  0.558864  0.544834  0.528560  0.522788   \n",
       "36  0.550358  0.555318  0.558822  0.558824  0.545432  0.529000  0.522040   \n",
       "37  0.550831  0.554436  0.559175  0.558660  0.544944  0.530481  0.523253   \n",
       "38  0.550477  0.554257  0.559695  0.558686  0.545223  0.529877  0.524979   \n",
       "39  0.550052  0.555326  0.559391  0.558846  0.545710  0.529863  0.527601   \n",
       "40  0.549518  0.554997  0.558406  0.558907  0.546154  0.529732  0.527177   \n",
       "41  0.549835  0.554926  0.558164  0.559831  0.545960  0.530565  0.527213   \n",
       "42  0.549775  0.554893  0.557636  0.560125  0.545169  0.529683  0.528194   \n",
       "43  0.550139  0.554451  0.557780  0.561023  0.545278  0.529981  0.529921   \n",
       "44  0.550489  0.554196  0.557850  0.561947  0.544759  0.530427  0.530030   \n",
       "45  0.550381  0.553776  0.557622  0.561816  0.544953  0.530094  0.530487   \n",
       "46  0.550014  0.553515  0.557524  0.561600  0.544135  0.529829  0.530489   \n",
       "47  0.550000  0.553200  0.557400  0.560300  0.544100  0.530200  0.530500   \n",
       "\n",
       "           0         0         0  ...         0         0         0         0  \\\n",
       "0   0.544918  0.532418  0.545569  ...  0.559139  0.562862  0.566181  0.533214   \n",
       "1   0.542150  0.531903  0.542782  ...  0.558227  0.563303  0.566003  0.534444   \n",
       "2   0.541996  0.530651  0.541626  ...  0.559085  0.561701  0.563658  0.535740   \n",
       "3   0.542917  0.530220  0.540387  ...  0.560940  0.562716  0.565614  0.537939   \n",
       "4   0.542500  0.530128  0.538860  ...  0.562211  0.561544  0.567048  0.539182   \n",
       "5   0.542863  0.528694  0.539164  ...  0.563748  0.563432  0.565731  0.539721   \n",
       "6   0.540997  0.531663  0.540026  ...  0.563025  0.564195  0.564848  0.540014   \n",
       "7   0.540982  0.532527  0.541069  ...  0.561238  0.564832  0.566200  0.540805   \n",
       "8   0.539605  0.533830  0.540687  ...  0.560343  0.565645  0.565486  0.542543   \n",
       "9   0.539561  0.535434  0.540825  ...  0.560809  0.566934  0.565715  0.543959   \n",
       "10  0.539281  0.537961  0.540807  ...  0.559856  0.565732  0.565627  0.544875   \n",
       "11  0.539038  0.536547  0.541973  ...  0.560710  0.563686  0.565868  0.544466   \n",
       "12  0.538992  0.536010  0.542385  ...  0.560493  0.562777  0.565372  0.543432   \n",
       "13  0.538102  0.536266  0.541978  ...  0.559644  0.562736  0.565987  0.542271   \n",
       "14  0.537730  0.536577  0.542592  ...  0.559656  0.563863  0.565787  0.542055   \n",
       "15  0.538479  0.535246  0.542235  ...  0.559100  0.563910  0.565111  0.542146   \n",
       "16  0.539035  0.534749  0.542595  ...  0.558679  0.563362  0.565695  0.543366   \n",
       "17  0.538552  0.534006  0.541979  ...  0.558655  0.562620  0.565537  0.544085   \n",
       "18  0.538266  0.534089  0.541570  ...  0.557948  0.562113  0.565035  0.544619   \n",
       "19  0.537573  0.533761  0.542021  ...  0.557837  0.562455  0.564521  0.543908   \n",
       "20  0.536976  0.532782  0.541775  ...  0.558044  0.562873  0.564368  0.544395   \n",
       "21  0.536723  0.532590  0.541605  ...  0.558666  0.562383  0.564101  0.544942   \n",
       "22  0.536235  0.532949  0.541562  ...  0.558682  0.562637  0.565331  0.545559   \n",
       "23  0.536379  0.532466  0.542206  ...  0.559271  0.562748  0.565249  0.545468   \n",
       "24  0.536137  0.532056  0.541930  ...  0.557741  0.562583  0.564636  0.544568   \n",
       "25  0.536185  0.531933  0.542049  ...  0.558180  0.561855  0.564653  0.545977   \n",
       "26  0.536445  0.531684  0.541927  ...  0.558658  0.562378  0.564587  0.545364   \n",
       "27  0.535519  0.530989  0.541983  ...  0.557250  0.562720  0.564767  0.544535   \n",
       "28  0.535890  0.530931  0.541601  ...  0.557437  0.562836  0.565326  0.544206   \n",
       "29  0.535709  0.529990  0.541232  ...  0.557901  0.563163  0.565048  0.544340   \n",
       "30  0.534904  0.530225  0.541799  ...  0.558380  0.562917  0.565170  0.544537   \n",
       "31  0.535283  0.529857  0.542656  ...  0.558490  0.563059  0.564879  0.544445   \n",
       "32  0.535856  0.529804  0.542008  ...  0.559286  0.562881  0.564713  0.543821   \n",
       "33  0.536072  0.530395  0.541624  ...  0.558712  0.562352  0.564654  0.543640   \n",
       "34  0.535673  0.531398  0.541741  ...  0.559140  0.561684  0.564264  0.544189   \n",
       "35  0.536719  0.531064  0.541214  ...  0.558707  0.561510  0.563619  0.545624   \n",
       "36  0.536369  0.532591  0.540442  ...  0.558695  0.561139  0.562779  0.547683   \n",
       "37  0.536591  0.532295  0.541021  ...  0.560097  0.561498  0.563312  0.548987   \n",
       "38  0.535902  0.532274  0.540298  ...  0.559908  0.561370  0.563463  0.549524   \n",
       "39  0.536555  0.531479  0.539844  ...  0.559289  0.561280  0.562858  0.549775   \n",
       "40  0.536593  0.532398  0.539048  ...  0.558897  0.561975  0.562833  0.550548   \n",
       "41  0.536433  0.533583  0.538532  ...  0.558934  0.561842  0.563104  0.549809   \n",
       "42  0.535575  0.532285  0.539913  ...  0.558865  0.560843  0.562838  0.550838   \n",
       "43  0.535989  0.532386  0.540099  ...  0.559340  0.561211  0.563181  0.550607   \n",
       "44  0.535728  0.532907  0.539244  ...  0.559143  0.561454  0.562820  0.550293   \n",
       "45  0.535418  0.532935  0.540304  ...  0.559027  0.561146  0.562837  0.549990   \n",
       "46  0.535061  0.532965  0.540850  ...  0.558711  0.560766  0.562524  0.549748   \n",
       "47  0.534900  0.532300  0.541400  ...  0.558300  0.560500  0.563000  0.550200   \n",
       "\n",
       "           0         0         0         0         0         0  \n",
       "0   0.559893  0.561648  0.560371  0.562463  0.562415  0.568874  \n",
       "1   0.560351  0.561581  0.557413  0.566676  0.561532  0.567762  \n",
       "2   0.560702  0.559512  0.558771  0.564838  0.560440  0.569919  \n",
       "3   0.559020  0.559046  0.558019  0.564294  0.561082  0.569098  \n",
       "4   0.558286  0.559681  0.557790  0.561706  0.561812  0.570103  \n",
       "5   0.557488  0.558984  0.557924  0.563692  0.561170  0.568935  \n",
       "6   0.556742  0.559672  0.557665  0.563556  0.565423  0.568568  \n",
       "7   0.556913  0.558741  0.557768  0.566072  0.562943  0.568123  \n",
       "8   0.557606  0.559012  0.558261  0.565603  0.561871  0.566811  \n",
       "9   0.557670  0.558950  0.559371  0.565010  0.560977  0.566489  \n",
       "10  0.557572  0.559567  0.558307  0.566039  0.562747  0.566040  \n",
       "11  0.557453  0.558998  0.559356  0.567064  0.563172  0.567427  \n",
       "12  0.557339  0.559001  0.561077  0.565854  0.563044  0.567474  \n",
       "13  0.557339  0.558947  0.561172  0.567269  0.562034  0.569174  \n",
       "14  0.557633  0.559404  0.563250  0.565443  0.563599  0.568217  \n",
       "15  0.557301  0.558942  0.563535  0.564344  0.564728  0.567956  \n",
       "16  0.557108  0.558663  0.561913  0.563932  0.566303  0.567791  \n",
       "17  0.557224  0.558933  0.562975  0.563955  0.566034  0.567380  \n",
       "18  0.557614  0.558582  0.562824  0.563642  0.565040  0.568044  \n",
       "19  0.557389  0.558317  0.562765  0.563222  0.564654  0.567993  \n",
       "20  0.557230  0.558579  0.562706  0.564368  0.564160  0.567490  \n",
       "21  0.556924  0.559004  0.563269  0.563505  0.565265  0.567519  \n",
       "22  0.557283  0.558864  0.562871  0.563499  0.565349  0.568001  \n",
       "23  0.557060  0.558909  0.561764  0.562941  0.565287  0.568623  \n",
       "24  0.556935  0.559008  0.560260  0.562051  0.563803  0.568173  \n",
       "25  0.556881  0.559449  0.560214  0.561728  0.563026  0.567816  \n",
       "26  0.557535  0.559116  0.560769  0.561344  0.562247  0.567339  \n",
       "27  0.556192  0.559385  0.561174  0.560838  0.561663  0.566780  \n",
       "28  0.555699  0.558922  0.561047  0.560621  0.561429  0.566543  \n",
       "29  0.555428  0.559219  0.560898  0.560975  0.561434  0.567200  \n",
       "30  0.555706  0.559684  0.562171  0.560663  0.561089  0.567007  \n",
       "31  0.555855  0.560518  0.561699  0.560779  0.560544  0.567084  \n",
       "32  0.555143  0.560097  0.561815  0.561285  0.560877  0.566475  \n",
       "33  0.555006  0.560146  0.561855  0.561331  0.560786  0.565807  \n",
       "34  0.555086  0.559272  0.561781  0.561284  0.560327  0.565160  \n",
       "35  0.555001  0.559188  0.561637  0.560099  0.560435  0.564524  \n",
       "36  0.555676  0.559275  0.560924  0.560814  0.561391  0.564584  \n",
       "37  0.555925  0.559366  0.561664  0.561785  0.560882  0.564934  \n",
       "38  0.557236  0.559984  0.561392  0.560708  0.561262  0.564667  \n",
       "39  0.556167  0.559655  0.561764  0.560796  0.560954  0.565410  \n",
       "40  0.556228  0.560308  0.561763  0.561381  0.560710  0.565127  \n",
       "41  0.555543  0.559797  0.561787  0.561190  0.560387  0.565715  \n",
       "42  0.554657  0.559401  0.561978  0.560715  0.561477  0.567075  \n",
       "43  0.554796  0.559493  0.562094  0.560651  0.562700  0.566993  \n",
       "44  0.554740  0.559041  0.562318  0.560624  0.561902  0.566917  \n",
       "45  0.554869  0.558475  0.561720  0.560480  0.561805  0.566409  \n",
       "46  0.554676  0.558111  0.561438  0.560766  0.561847  0.566404  \n",
       "47  0.554400  0.558400  0.561800  0.561600  0.561400  0.566600  \n",
       "\n",
       "[48 rows x 31 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取最新公开的持仓数据\n",
    "T = 1\n",
    "def get_clean_values(day):\n",
    "    u_data_ = pd.DataFrame()\n",
    "    try:\n",
    "        for data_every in data['日期']:\n",
    "            print(data_every)\n",
    "            values_ = pd.DataFrame()\n",
    "            one_all_before = data['单位净值'][data[data['日期']==data_every.split(' ')[0]].index.to_list()[0]+1]\n",
    "            data_every = data_every.replace('-','')\n",
    "            for i,j in zip(kk['股票代码'],kk['持仓占比']):\n",
    "                b = ef.stock.get_quote_history(i,beg=data_every,end=data_every,klt=5)['收盘']*j*0.01\n",
    "                if len(b) == 0:\n",
    "                    return u_data_\n",
    "                values_ = pd.concat([values_,b],axis=1)\n",
    "            one_data_ = np.array(np.sum(values_,axis=1)/one_all_before)[-1]\n",
    "            u_data_ = pd.concat([u_data_,(np.sum(values_,axis=1) / one_data_)],axis=1)\n",
    "    except:\n",
    "        return u_data_\n",
    "    return u_data_\n",
    "\n",
    "clean_data_ = get_clean_values(public_dates[0])\n",
    "clean_data_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "6eabdb79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75\n",
      "75\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAikAAAGbCAYAAAABeQD9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAACOx0lEQVR4nOzdd3hUZfbA8e+dSe+V9EZvCQESpCiIglgA+yp21LWtHdHV38rKrgvq2rCvZUURu6tgASsgTSBIICEQICQhvSeTOsmU3x83GYikt5kk5/M880jmtjMSZs6897znVcxmsxkhhBBCCBujsXYAQgghhBAtkSRFCCGEEDZJkhQhhBBC2CRJUoQQQghhkyRJEUIIIYRNkiRFCCGEEDZJkhQhhBBC2CRJUoQQQghhk+ysHUBXmUwmcnNzcXd3R1EUa4cjhBBCiA4wm81UVlYSHByMRtP2WEm/TVJyc3MJCwuzdhhCCCGE6IKsrCxCQ0Pb3KffJinu7u6A+iI9PDysHI0QQgghOkKn0xEWFmb5HG9Lv01Smm7xeHh4SJIihBBC9DMdKdWQwlkhhBBC2CRJUoQQQghhkyRJEUIIIYRN6rc1KUIIIWyL2WzGYDBgNBqtHYqwMq1Wi52dXbdbhEiSIoQQotvq6+vJy8ujpqbG2qEIG+Hi4kJQUBAODg5dPockKUIIIbrFZDKRnp6OVqslODgYBwcHabI5iJnNZurr6ykqKiI9PZ0RI0a027StNZKkCCGE6Jb6+npMJhNhYWG4uLhYOxxhA5ydnbG3tyczM5P6+nqcnJy6dB4pnBVCCNEjuvptWQxMPfH7IL9RQgghBOptip07dzZ77vjx4xw8eLDVY7744gsaGhosP6elpZ12jrZkZmZ2av/S0tJmP9fW1g7oOiBJUoQQQgjUBODSSy9lx44dlueeffZZ/vKXv7S4/++//84VV1zBrl27LM/98MMPXHvttdTV1bV4TElJSbOf//rXv3LfffdhMBjajS8nJ4fAwED27dtneW7t2rWMHTsWs9nc7vH9kSQpQgghBODr68vy5cvZunUrADU1NXz88cesWrWqxf0/+eQTJk+ezIwZMzCZTADcdtttxMTEcPz4cct+Tds+++wzJkyYQGpqquXnL774gsLCQoYPH46Pjw9+fn5kZma2eL21a9cyc+ZMvvvuO8LDw4mMjOSvf/0r+fn5REVFERkZSUhICN98802P/T+xOnM/VVFRYQbMFRUV1g5FCCEGtdraWnNKSoq5trbW2qF02V//+lezp6dns4ebm5tZURTLz2vXrrXsX1tba/b19TWvXLnSnJycbLazszN7eHicdg4PDw/znDlzLMf94x//MI8YMcK8fft2s7e3t/l///uf2Ww2m+vq6swTJkwwf/755y3GV1VVZQ4ICDB//fXX5vr6erPBYDCbzWbzP//5T/NVV11l2U+v11u2WVtrvxed+fyWkZQ/KiuDf/8bbrnF2pEIIYToIwaDgfvvv5/y8nLKy8s5//zzefnllzGZTJSXl3PmmWdSX19v2f/111+npKQELy8vxo0bR0NDAxUVFZbjy8vLycnJoaKigh9//NFy3OOPP86vv/7KQw89RFxcHDfffDPh4eH4+/uTnp7OkiVLCAoKYvHixc3ie+qppygoKCAwMBB7e3tuvfVW4uLieOWVV/j555+Ji4sjLi6OhIQEtFptn/1/620yBfkPSqv0vPt1Emk+IbyWlQVhYdYOSQgh+h+zGaxV0OniAp3s06LRaHjxxRdZvXo1AEVFRfz000888cQTABQWFnLFFVdYtq1YsYLIyEjL8d9++y3btm1j5cqVgFrQ6u3tzZo1a7jqqquaXSswMJBNmzbh6Ohoec7NzY3k5GQiIyN59tln2b9/v2Xbnj17eP755/H29rY8l5aWxpNPPsn5559vee78889vtRamv5KRlD9QfHx4bdqVfDf6TDLXfW/tcIQQon+qqQE3N+s8upAcKYrC/fffT0ZGBhkZGSxYsIBnn33W8vM555xj2ffNN99kxowZzJo1y/Kcj48Pr7/+umW0JTk5GZPJxNy5c1u8ntlsRq/Xt1rwqtFoqKqqAtRalAceeIDg4OBm22+++WaGDx9ueWzdunXATQOXkZQ/8HZ1YJpdNduM7mzcm8nt1g5ICCFEr+vMekP3338/FRUVPPbYY5bnpk6diqenJz/88APz588nISGBM844Ax8fn9OOf/rpp8nKymLdunWWWzM1NTWceeaZ2NnZodPpqK+v5/vvvyczM5Onn34ajUbDV1991ew8//3vf08bSRloJElpwbyJYWxLKGeD2Yfb6+qgi53yhBBi0HJxgcaRAKtcu5Pc3d3573//2+rtHoCbbroJAFdXV1xdXZsdrygKCxcu5LvvvmP+/Pns3r2bc88997TrfP/996xatYojR47w73//GycnJxRFwc3NjW3btllu9yQnJ1tiac0dd9yBm5ub5efWZgX1Z5KktGDeeZNZtudHEgNHkrdxE0GXXGDtkIQQon9RFPjDB7ktMpvNVFdX8/DDD/Pwww9bnr/hhhuYO3cu119/fbN9dTodHh4eLZ7r3HPP5aGHHgJg69atvP3226dd629/+xuPP/44+fn5nHnmmTg6OqIoSrORFICLL7643djfeOMNGUkZjIZ4OBFnKGOPvS/fb0nmJklShBBiQCooKCAsLOy0RRH1ej3r16/ngQcesDzX0NCAg4MDlZWVzc5hMpnQaDSce+65rFq1ioyMDHJycpg6dWqz/d5++21KS0u59dZbsbe3Jz8/37Lt1JGUjpKRlEFs3ghv9mTAhgp7bjKbO10pLoQQwvYFBgY2a2vf5Morr+TCCy88bSrwqYWuDQ0NmEwmrr32Wj799FNLkmM2mzGZTJYEwmQycfz4cQIDA3nmmWewt7c/7ZwdqYkxGo3o9XpLd9qWRlKaCnJPnTnUnw2sMuAedP6C6QDs8RtK8e/JVo5GCCFEX6qqqmoxeTl1tEWn09HQ0MD7779PQ0MDBoMBg8GA0WjEbDZbfq6vryciIoIFCxZw+eWXNzvfyy+/TExMDECzKcYtqa+v56effiI8PJz09HTuuOMOIiMjLY/Dhw9z4403tjqjqD+SkZRWhIb4ElNTwAGXAH787jcWTY62dkhCCCH6yIYNG9rd5+uvv+7QuZrqTFoyZ84cvLy8mDVrFp6enm2eJy0tDYC///3vHbruQCBJShvmBdpzQAcbcvQssnYwQgghBpwxY8YwZswYa4dhs+R2TxsuuCAegB3uYVTkF1s5GiGEEGJwkSSlDUMnjmaULg+D1o6fv9xi7XCEEEKIQUWSlHbMc9MDsOGwjKQIIYQQfUmSlHZccJZ6r/BX+yFU1+itHI0QQggxeEiS0o7R884ksiIfvZ0Dmzf8Zu1whBBCiEFDkpR2KA4OzDOrt3o2JGRYNxghhBBiEJEkpQMumBgGwKYGd+oaOr5SphBCiP6ptraWqg4ukJiamsqhQ4c6fO4lS5Zw+PBhy8979+5lzZo1nY7xj1pqPmfN8/QESVI6YMKlcwjSFVFt58i2XanWDkcIIUQP27ZtGxMnTrT8/PHHHzN69Ghqa2vbPfYf//gHL7/8coeuc+zYMV599VW8vLwsz7344os88sgj1NXVdTruJhs2bGix0+ykSZPQarXY2dm1+FAUhaysrGbHzJ07l40bN3Y5lp4kSUoHKEOGME93HICNWw5aORohhBA9Qa/X8/bbb1NXV4eTk1Oz9W7Wrl3L448/jrOzs+W5prV6mowYMQJXV1e+/fZbPvzwQ7y8vCwPJyenFq/51ltvsWjRIgIDAwE4ePAgP/zwAwsXLrSsoNxZv/32Gw8//DDvv//+adu0Wi3vvPOOpUX/qY9jx44Bp3fEfe+991i6dCm//Wb9OsxOJynFxcVERUWRkZHRof23bNnCmDFj8PPz4/nnn2+27fPPPyciIoLg4GA++uijzobSpy4Yrq6p8GO5hnqDqZ29hRBC2DqDwcCf//znZuvxACQnJ/Prr7/yf//3f/j5+eHm5oazszNubm78+OOPlv1cXFxYu3YtJSUlXHrppaxfv57y8nKys7NbXOCvrKyMt956i8suuwyAuro6Fi9ezKuvvsorr7zCgQMHePTRR5stYtieuro6br31Vj755BPCw8NP267Vats9xx+TlIiICD7++GNuueWWDo0k9aZOJSnFxcXMnz+/wwlKUVERCxcuZNGiRezcuZO1a9eyadMmQP0luPbaa3n88cf5/vvvWbZsGamptnsrJW7+TIZUllChdeLNX2w3TiGEsAVms5maeoNVHh39kG9KJP6YUDzxxBM888wzFBcXU1xczPXXX8+zzz6LXq9nzpw51NTUYDAYLMmNVqvliiuu4K233rJcW1EUzGZzsw/5f/zjH5SVlWFvb091dTWXXnopkyZN4oorrsDOzo5169bx448/ct5553W4xuWjjz7i/PPPZ+zYsS1u/2MC1pKWEplx48Zx/vnn8+GHH3Yojt7SqbV7rr76aq655hp27drVof3Xrl1LcHAwjz/+OIqisGzZMt555x1mz57N22+/zezZs7n11lsBuPvuu1mzZg1PPvlk519FH9BOnsSjD67kgemLeemXNM6PDWP4EDdrhyWEEDaptsHI2GXfW+XaKf+Yh4tD15am27JlC3v37mXnzp3MnDmTSZMmkZWVxYUXXgjA/v37mTVrFvb29lRUVLB48WLL5xiAv78/ZrOZyspKfH19MRqNVFRU8NNPP/Hf//6X+Ph46uvrmTZtGmeffTaXXXYZ1dXVuLq6UlxczNNPP83q1at57LHH+PLLL9uN99tvv+Wvf/1rq9s7kqS0ts/VV1/NihUruOWWW9o9R2/p1EjKW2+9xb333tvh/ffv38/s2bMt/wOmTJnC3r17LdvOOeccy76nbmuJXq9Hp9M1e/QpReGSy2dydloC9Sg8+vl+TKaOD8kJIYSwfSkpKfz73/9m9uzZbN68GYADBw4QExMDqIWolZWVpKWlYW9vT1paGh9//DHLli0jKyuL4uJiDh48SFhYGKWlpVRUVACQnp7OP//5T4KDg3FwcOD999/npZde4uqrryY/Px+AjRs38t///pc1a9bw+eefdyje7OxsIiIiWt2uKAr33HMPfn5+pz0mTZpk2aclERERpxXV9rVOpZpRUVGdOrlOp2s2BOXh4UFubq5l26nnO3VbS1auXMny5cs7df2eptz2Z/71chznhY5lzwlYuyuT66dFWjUmIYSwRc72WlL+Mc9q1+6qO++8E1A/uFetWsX8+fPRaDTNEgGj0ch9993HBRdcgI+PD6NGjeKjjz7itttu47777uOTTz7Bza35SPuf//xnAH755RcAYmNjAaivryc4OBgAe3t77O3tgY7VkoD62anT6fD3929xu6IovPzyy9x0002nbcvIyGjzc12n0+Hp6dmhOHpLr87usbOza3avz8nJiZqamna3teTRRx+loqLC8rBKdufsTMjdf+bhX9UK6qc2HCan3LpFRUIIYYsURcHFwc4qj47c4gDYvXs3AAsWLODqq69utu2iiy7i4MGDLF269LRtRUVFGI1GXn31VQDCwsJ45513ePvtt3F3d6ekpISXXnqpw/+vTp1B1Fljxoxh586drW7vSH1Oa/v89ttvrda69JVeTVJ8fHwoKiqy/FxZWYmDg0O721ri6OiIh4dHs4dV3H471+ckEJd9kOp6I3/7MqlTldhCCCFsg5+fH3fccQcPPfQQH3zwQbNtTk5OXHHFFXzzzTfcddddzbYFBgby0UcfMWXKFCIiIrCzsyMyMpKIiAgmTZrEL7/8wp/+9CfOO++8Nq9//PhxgoKCuvUarrvuOl588cVmU6NP1dUkxWQysWrVKq677rpuxdddvZqkxMfHN8vw9u3bR0hISLvbbJqzM5qHl/LUhpdxMDawKbWI9ftbv00lhBDCNo0cOZLXX3+dWbNmnTYNd+PGjXz44YcEBgayatWq05IAZ2dntm3bRmpqKoGBgWRkZPDxxx9zwQUXkJGRwVNPPdXuCMmbb77J7Nmzu/Ua4uPjGTNmDMuWLWtxe2vJy6mMxtM7qS9btoyRI0dyxhlndCu+7uqRJEWn07XYRnfhwoVs376dn376iYaGBp555hnmzVPvUV5++eV8/PHHJCUlUVVVxUsvvWTZZvPuuIPhdvXcs/1jAJ5Yf5CSKlkhWQgh+qum0YTy8nL+7//+jyuuuIIXX3yR33//nV9++YVp06bx2WefWcoSujprpq6uDrPZzJdffskbb7zBgw8+aNlmMpm6NDL/yiuv8OOPP/K3v/3ttG1Go7HdwlmDwdDsmL/97W/8+OOPvPbaa52Opaf1SJISExPDt99+e9rzfn5+vPDCC1x44YUEBASQmppq+Z84YcIE7rvvPuLi4ggJCUGr1Z42pGazXFxg6VJu3/UFo8tzKKtp4B/fpFg7KiGEEF2k1+vR6/Xcd999fP3112zdupVbbrmFgIAAdu7cyYIFC3jhhRf4y1/+QmBgIGazmZkzZzJ69GgqKyuJjIzk2muvZdOmTURGRvJ///d//Pzzz3h5efH1119brlNfX09NTQ0vvvgiq1evZujQoafF0Fmenp5s2rSJyZMnn7bNaDTy8ssvW3q+nPr4/fffgdOTlMmTJ7Np0yarF80CKOY+KKhIT0/n8OHDnHXWWadVPKekpJCTk8OsWbParEn5o6aq44qKCuvUp1RXQ1QUB7SeXHLjC5hQ+PyOacRF+vR9LEIIYUV1dXWkp6cTFRXVajv4/sJgUJvBNc2y6S1ms7nDBb79VWu/F535/O6TtXuioqK44IILTktQAMaOHcvcuXM7laDYBFdXWLqUmPxjXJmurm/w7vYM68YkhBCiW+zs7Ho9QYGO3S4SssBg99x1F/j5sfgXtSp848F8cmVKshBCCNEjJEnpDldXeOghRhdnMq3wCEaTmTW/ZVo7KiGEsAppxyBO1RO/D5KkdNdf/gK+vty07VMAPtp9grqG06dzCSHEQNV0e6Sthpxi8Gn6fejO7bOurcAkTnJzg8ceY85DSwmpKiYHP9Yl5nBV/OlLZgshxECk1Wrx8vKisLAQABcXF6m5GMTMZjM1NTUUFhbi5eXV4Rb/LemT2T29weqze06l18Po0bw5ZCIrZt/C6EB3Ntx3lvwjFUIMGmazmfz8fMrLy60dirARXl5eBAYGnvZZ2JnPbxlJ6QmOjrBiBVct/jMvnHkth/NhV3opU4f6WjsyIYToE4qiEBQUxJAhQ1ps7ikGF3t7+26NoDSRJKWnXHUVns8/z2XJv7B24oW8uz1dkhQhxKCj1Wp75MNJCJDC2Z6j0cCzz3LTXrWz4I8pBWSVShGZEEII0VWSpPSkWbMYMT2WMzP2YTLDBzIdWQghhOgySVJ62lNPcdPv3wDw0c7j1NQb2jlACCGEEC2RJKWnjR3LObMnEFGWi64BvtqXY+2IhBBCiH5JkpReoFm+nBuSfwRg9YYD0oVRCCGE6AJJUnpDYCBXzonGpb6WI3Uadh8ttHZEQgghRL8jSUov8Vj6AOfmHABgzw+/WTkaIYQQov+RJKW3uLkRM8QVgKQcnZWDEUIIIfofSVJ60fgwbwCS67u+uJIQQggxWEmS0ovGTRgGQI69O6XV9VaORgghhOhfJEnpRR6TYogqVacgJ6UVWDkaIYQQon+RJKU3+fszvjwbgOT9aVYORgghhOhfJEnpZePt9QAkZ5ZYORIhhBCif5EkpZdFBzTO8KmUhm5CCCFEZ0iS0svGjQ4FIFtxpkyKZ4UQQogOkySll3nGjieiLBeA5NwKK0cjhBBC9B+SpPS2ceMYn38MgKTUXCsHI4QQQvQfkqT0NldXohvKAEg+KkmKEEII0VGSpPSBaC87AJKL9VaORAghhOg/JEnpA+OGDgHghNGeipoGK0cjhBBC9A+SpPQBr+gxhJXnA1I8K4QQQnSUJCl9ITqa6Kbi2exy68YihBBC9BOSpPSFESMYX5wOQNKRPCsHI4QQQvQPkqT0BXt7oh3UWhS53SOEEEJ0jCQpfWR8qCcAmXUKFbVSPCuEEEK0R5KUPuI9bhShjcWzB3NkNMXiyBH461+hqMjakQghhLAxkqT0lfHjiS5IA+SWj4XZDDfcAE8/Dbffbu1ohBBC2BhJUvpKdPTJ9vhZZVYOxkZs2gS7dql//vJL2LjRuvEIIYSwKZ1KUpKTk4mPj8fb25ulS5diNpvb3L+hoYGlS5cSHh5OUFAQy5Ytw2AwAGA2m7nzzjvx8fHBy8uLm266idra2q6/ElsXFsb4SnVmT3JGiZWDsRErVpDt4c+r59yIzsEF7rkH9NKVVwghhKrDSYper2fBggVMnjyZhIQEUlJSWL16dZvHLF++nA0bNrBx40a+++471q5dy/LlywFYs2YNqamp7Nu3j61bt3Lw4EFWrlzZrRdj0xSFaF9HANIrDejqBnnx7K5dNGzazK2XL+Pf8Vfy6KUPw7Fj8Oyz1o5MCCGEjehwkrJhwwYqKip4/vnnGTZsGCtWrOCdd95p85j333+f5cuXM3bsWCZOnMiSJUtYt24dALt37+aKK64gIiKC6OhoLrnkEo4dO9a9V2PjfMYMJ6SiEICDOTorR2NlK1bw5pTLODwkCoBvI+P4YfgZ8K9/QUaGdWMTQghhEzqcpOzfv5+pU6fi4uICQExMDCkpKW0eU1xcTHh4uOVnrVaLVqsFYNy4cXzwwQcUFBSQmZnJxx9/zNy5c1s9l16vR6fTNXv0O6fUpSQP5hk+SUkc2/Y7q2YsAmBiuBcAjy+4nwqTBh54wIrBCSGEsBUdTlJ0Oh1RUVGWnxVFQavVUlbWehHopEmTLCMnRqORNWvWWBKRW2+9laqqKgIDA4mMjCQqKoobb7yx1XOtXLkST09PyyMsLKyjoduO8eOJLmgsnh3ESYrpqad49Px7qLdz4OxR/nz056lE+blS4ODOU7Nvhq++gg0brB2mEEIIK+twkmJnZ4ejo2Oz55ycnKipqWn1mFdffZX33nuP8847j5EjR7Jr1y7uvPNOAFatWoWXlxeZmZmcOHECg8HA0qVLWz3Xo48+SkVFheWRlZXV0dBtx6kjKdmDdIbPsWOsPVzBnrBxuNopPHnJeJzstTx1WTQAH02Yx86waLWItq7OysEKIYSwpg4nKT4+PhT9oeFWZWUlDg4OrR4zYcIEMjIyeOGFF/D09GTx4sWW0Zi1a9daZv6EhYWxcuXKNmtcHB0d8fDwaPbod3x8GK9UA5BeUkuV3mDlgPpezrMv89RMdcTs4QvHEuqt3j48Y6gv156h3hp8dP791GZmSxGtEEIMch1OUuLj49m5c6fl5/T0dPR6PT4+Pm0ep9VqqampITU1lSeeeMLyvMlkorCw0PJzfn4+RqOxE6H3T37DIwjSFWFm8HWeNWdl8bdSH6odXZjsreX6qRHNtv/1gtEEejiR4RHAi2deAytWSBGtEEIMYh1OUmbOnIlOp+Pdd98FYMWKFcyZMwetVkt5eXmbCcayZctYsmQJwcHBlufOOussnnrqKVavXs2bb77JXXfdxcKFC7vxUvqJ6GiiG2/57Msqt24sfWz9i2vZNDQOB5OBpxfPQKNRmm13d7LnyUvGA/DWlEtJ8giGW24Bk8ka4QohhLCyTtWkvP3229x99934+fmxbt06nn76aQC8vb1JSkpq8bgtW7aQmJjIww8/3Oz5J598kmnTpvHwww9z3333ER0dzapVq7rxUvqJ8eOZnrkfgC2ptr9ejdFkRm/o/ghXaWYuyw3qyMk9wxwYPsS9xf3mjA1gwYRgTIqGhy96gIbNW+S2jxBCDFKKub22sX+Qn5/P3r17mTp1Kr6+vr0VV7t0Oh2enp5UVFT0r/qUvXvJmLOAs29/CzuNwr5lc3F3srd2VC0yGE3Mf3kb5TUNfHzbVCL9XLt8rvsffoevNIGMrsxn/Ys34WCvbXXf4io9c57fQnlNA3f89hl/3b4Wdu6EuLguX18IIYRt6Mznd6fX7gkMDOSiiy6yaoLSr40dS2RFPlGlORhMZrYfs90W+duOFXM4v5J8XR03r95DeU19l87zyysf8pUmEI3JyNPT/dtMUAD83Bz558XqbZ83pl7Jp2NmwzXXQFVVl64vhBCif5IFBvuaszMMH86s43sB2Jxa2M4BrSvbn8LdVz/BD4+/2EPBNfe/33Msfz5eXM1ta/Z2+tZP1dff8bcUNbm5RZPHhGs7Vne0YEIw95wzHIDHzr+b7fUucN99nbq2EEKI/k2SFGuIiWH28QQANqcWtbtQY2u++HI730TG85e6SHa89WlPRoiuroHvD+YD8Ez5btzsNexOL+XRL5I6Hm9CAs+8voFcD3/CDVU8uPyWTsXw4NyRXBwbjEGj5Y5LHuXouh/h0559nUIIIWyXJCnWcOGFnHEiCSdjPfm6Og7nV3bpNPuK1RGKBq09dxw0c2z7vh4LcUNSHnqDieHFJ7jyP//gtU+Xo8XM//blsOrno+2fIC2NPYvv5/2Y8wFYectMnB3tOhWDoig8fXkM8ZHeVDq5sfiKv1N030Nw4kRXXpIQQoh+RpIUa7jkEpwUM9My1Fk+m7s4yydRUQuOAmrK0Tm5sfjjJIpze2bG0Bd71Vs9lyX/guLpycwju3hy4ysAvPjTUb7cl936wUVF1F04n0emXgfAVRMCmTEmqEtxONlr+c/1cUT6uJDtFcitc++n9oabYBD01BFCiMFOkhRr8PGBuXOZndZ0y6fzdSlF+aXkuPqgmE18dl00EZWFZLn68uenv6ZO39Ct8E6U1LA7oxTFbOLSlE2wZQusWcOi3N+5/bfPAXj4433sSjgKtbXNH2VlMH8+rwTEcdw3DH8XOx67OKZb8fi4OvDuzVPwctSwP3gUD3hPxfTiIJiuLoQQg5wkKdZy5ZWc3ViXkpBZhq6uc4lF4g61L83winzCp8Tw3yvG4FlXyT7nAB7856eYTF2rcwH4cp86ijIjcz9BZ0yECRPguusgNZVHRjlw4eFtNCgaFn+4n79dvISkqGjMLi7g4gI+PqSkF/HG1CsA+OdlMXi6dH+KdZSfK2/edAYOmNk4agZv/dYP124SQgjRKZKkWMsllxBeU8rQkiyMJjPbjxZ36vDEw2oiEWtSW+sPmz2V/wyrx97YwHcGL555Y2OXwjKbzfxvr1rzcXnSz/Dggyc3enuj+c9/eH7JfM4oTafGwZkPJl3EgptWceFNL7F60nxKnD14ZOESDBo7LhgfyPnju3abpyVTonx4dIbatfg7l3Coru6xcwshhLA9kqRYi7c3zJ3L2Y1TkTd18pZPYolaNBvre3KBx6n33MAzNWqdyxsnTHz6fWKnw9qbWUZmWR2u+hrmaUph3rzT9nE6awYfvX4Xa6+NYeG4IThoFQ4FDOWJuXcQf9+HJPlF4uFkx/KLx3X6+u0576wxACQHDKNm+8529hZCCNGfSZJiTX/6k2Uq8pYjHZ+KbDKZOaCobeVjRwc323bpc49w7/HNALy28WCnQ/oiQb2NcsGR7bjcezdoWv4V0Wg1zIgO46Xr49n9f3N4YsFYxgR50HSX6W8XjWWIu1Onr9+eEC9ngg3VGDVaErcm9vj5hRBC2A5JUqzp4ouZkp+Kc30dBTo9h/I6NhU5rUBHpZ0TzvV1jDojuvlGJyduWXI1ABmOXpTqajscTl2DkW/2qUnKZVl71TqUDvByceCmGVF8d++ZfHPPmbx38xSujAvt8HU7K85TXZhwT3ppr11DCCGE9UmSYk1eXjjOOZcZjQsOdvSWz76EVACii45jN2L4ads942IZWpYLwP7fOj6a8tOhAiqNCiEVhUy95By1O24nKIrC+BBPZo30R1GU9g/oovjocAASjG6g1/fadYQQQliXJCnW9qc/Mavplk8H+6XsayyanWiqAG0L6+BotcQ2qKMM+w6kdziUL35OBuDSw1vQ/OWuDh/X1+KmjAbg98ARGHbvsXI0QggheoskKda2cCFnZx8AYG9mKRW17U9Fbqlo9o8m+qjdXRMLajoURmFlHb/mq6MSl47whICADh1nDaMCPXA36ql2dOHwlgRrhyOEEKKXSJJibZ6ehE2bxPDiExjNsK2dqcg19QZSzS7A6UWzp4odHQLAfpNrhwpy1/+SjFHRMDHnMMPu/XMnXkDf02gU4lwMAOxOzbdyNEIIIXqLJCm24JRZPptSC9rcNSm7ApOiIaCyhKC41ju5jj5zIo4NeirsnUkv0LUbwhfbjwFwmSkPxo/vRPDWETcyEICEWntpkS+EEAOUJCm2YOFCzs5Sb/lsOZjXZrfYxBR19k1s3pE2kwn7kSMYX5yhHrMrpc3LHz9RxCGNO/bGBhYsmtPJ4K0jfqraL2VP4EjMiYnWDUYIIUSvkCTFFnh4EDc+DJf6WorqTKTktT7yYek0ayhV29C3RqMhVlGnNO9LaWMxQGDT92rx6RlFx/C66PTmbbYoJtwHB5ORIjcfTmz+zdrhCCGE6AWSpNgIxyuvYEZGIgAbkvJa3a8jRbNNYoeoSUxiaX2b+20+WgLA2e4G6MWpwz3JyV5LtH0dAHuSTlg5GiGEEL1BkhRbsWABlx3ZBsB7245TVn16YlGgqyPP7IDGZCRmVEi7p4yNjgTgkOJOXUPLdRs19QZ24QHA2XFDuxi8dcRF+QCQUGGGDnbrFUII0X9IkmIr3N2ZN8KbsQVpVBnM/OfX46ftsu9EOQAji0/gOrH1otkmodMn4VddhkGj5WBmSYv77NybRr3GjtDyfIZdcHZ3XkGfi5/SWJfiNxQOH7ZyNEIIIXqaJCk2RHPzzSzZ+gEAq7enU1hZ12x74nG1I+3E3FSIjW33fMrQocQWq83c9u1p+UN80za1qHa2LgNlyJCuhm4Vk4f5A5DmG0bJpm1WjkYIIURPkyTFllx0EedQSmzuYeoMJl7blNZsc+IRtVYltjq/Y83WFIXYxrqNxGOnT202m81sLlSbx50d4trN4Puet6sDIxR1baKE349ZORohhBA9TZIUW6LRoNx3Hw/9ugaAD3dlkluufggbTWaSmopm/dovmm0yMUStN0nUnV6zkVZYRbbWFQdDPdPOmdjd6K0iPsQNgIRCvdSlCCHEACNJiq256SZmlKUzNfMA9UYzL/+ijhAcLayk2qzBVV/D8FHhHT5dzKQRKGYT2VpXiiqbL8a3ebt6q+eM7IO4zDqr515DH4qPGwnAHq8wyMy0cjRCCCF6kiQptsbdHeXWWy21KZ8lZJFZUk1iY9FsTP5RtBNjO366M+IYXqI2gEtMa77K8qb96vNnK+Vt91yxYU2dZ5MDhlO7+VcrRyOEEKInSZJii+65h/i8w8w6noDBZGbVT0fZl1kGQGzukQ4VzVqEhRFbqvYRSdx3sm6jWm9gd51622j22MAeC72vhXo7E2jWY9DakbjrkLXDEUII0YMkSbFFkZFw6aWW0ZQvE3P4OaWxaLY0A4YP7/i5FIVYF7VHSmLGyWnI248U0qBoCS/LI+q8/nmrB0BRFOL8HQFIyPlDp16zmYwvN3L7XS/zybMfWCE6IYQQ3SFJiq26/35i8o9x3rFdmM1QXKsmGhO97UCr7dSpYiN9AThQa2dZF2jzdnXUYXZOEkpcXA8G3vfiJ6pN6HY7B0J+vlpA++OP/LBwMQt+1fG9x1BWZUpRrRBC9DeSpNiqGTNg8mQe3PI+CuoHbLCukCFjOzGK0mhU/Fic6+uo1NiTVlSF2WxmS6Y66nC2N51OemxN3JhgAH4PHo3h2ecwnDWTp/61ltvGXUmlozq1OtfVh7qqGmuGKYQQopMkSbFVigL338/o4kwWpO8GYGLO4c7VozSyi48jukCtR9l3rICjhVXkmB1wbNAztXE14f5sdKAHbmYD1Y4ubPvfL1wfdgFvTL0SgJsnDsFdryYnJ5Kkl4oQQvQnkqTYsj/9CQIDeeLrVdz+2+dqjUoXkhSCg4nVqasnJx5IZ3OSuiry1KwknM87twcDtg6tRmGSrz0At1zxd3ZGTMDVXsMr10xk2VXxRNapRcfpR2QhQiGE6E8kSbFlDg7wl7/gU6vj0S2rGVqeB9HRXTrVRE/1rzoxp5JNCWqr/NmlxztXhGvD4iePAMCo0TJ8iBvr7jmT+THqbaBIjdofJiO72GrxCSGE6DxJUmzd7beDozp7hZEjwbVr7etjR6jTjA832JNQphbhnh3lqd5WGgAuignC392RSyeGsO4vMxg+xN2yLcrdDoCM0lprhSeEEKILJEmxdf7+cN116p8nTeryaYKmTCCgsgSToqFB0RBZmkvkudN7KEjrG+rvxu7HzuWFq2JxdbRrti1yiLo0QEad/LoLIUR/Iu/a/cFTT8FDD8ETT3T9HJMnE5ubavnx7OMJcM453Y/NhiitjApFRKmjSBna/reIohBCDGadSlKSk5OJj4/H29ubpUuXYm5nQbeGhgaWLl1KeHg4QUFBLFu2DIPB0Gwfk8nE9OnTee655zof/WDh5wf//rd6u6erhgwhtvbkSshnUwpDhvRAcLYvavwwAPJcvKmrqbNyNEIIITqqw0mKXq9nwYIFTJ48mYSEBFJSUli9enWbxyxfvpwNGzawceNGvvvuO9auXcvy5cub7fPGG29QUVHBvffe26UXIDpuop9a2+LUUMfU2CgrR9N3vIeG4VFXBUDmweNWjkYIIURHdThJ2bBhAxUVFTz//PMMGzaMFStW8M4777R5zPvvv8/y5csZO3YsEydOZMmSJaxbt86yPTc3l8cee4yXX34Ze3v7rr8K0SFTxobylx2f8PSGl3GaM7Bu9bRF0WplGrIQQvRDHU5S9u/fz9SpU3FpXC03JiaGlJSUNo8pLi4mPDzc8rNWq0V7SnfT+++/n4iICLKystixY0eb59Lr9eh0umYP0Tma+DiWbl3DxWk74az+u15PV0QqjdOQs2QashBC9BcdTlJ0Oh1RUSdvESiKglarpaysrNVjJk2aZBk5MRqNrFmzhrlz5wKwc+dOPvvsM0JDQ0lLS+PGG2/k7rvvbvVcK1euxNPT0/IICwvraOiiyaxZcO218K9/dXkqc38V6aYmx5kl0hpfCCH6C7v2d2nc0c4Ox6Z+HY2cnJyoqanB29u7xWNeffVV5s+fz+7du0lLS+PEiROsWbMGgLfeeoszzjiDb775BkVR+POf/0xERAT33HMPo0aNOu1cjz76KA8++KDlZ51OJ4lKZzk4wAeDczXgqCHuUALpdQOjL4yt2nGsmITMMm6aEYmHk9zCFUJ0T4dHUnx8fCgqKmr2XGVlJQ4ODq0eM2HCBDIyMnjhhRfw9PRk8eLFltGY7OxsLrzwQsu00bCwMPz9/UlLS2vxXI6Ojnh4eDR7CNFREZEBAGRoXKwcycBVpTdw+5q9PP/jES5+ZTuH8+WWrBCiezqcpMTHx7Nz507Lz+np6ej1enx8fNo8TqvVUlNTQ2pqKk+c0ucjNDSU2tqTHUCrqqooLS0lJCSkE+EL0TFR44YCkO/sRW1dg5WjGZj+93s2lXq1xUB6cTWXvLqdL/dlWzkqIUR/1uEkZebMmeh0Ot59910AVqxYwZw5c9BqtZSXl2M0Gls9dtmyZSxZsoTg4GDLc4sWLeKtt97i559/JjMzk7vuuovRo0cTExPTjZcjRMu8Rw7Fs7YSgMzD6VaOZuAxmcys3pEBwP1zRnDWCD/qGkw88Ml+Hv8qGb2h9fcHIYRoTYeTFDs7O95++23uvvtu/Pz8WLduHU8//TQA3t7eJCUltXjcli1bSExM5OGHH272/Ny5c3n66ae58847GT16NEePHuXzzz9vtWuoEN1iZ0dkbSkAGYdlGnJP+/VoEceLqnF3tOPWs4ayevEU7j1XXfRxzW+ZXPWf38gtl7WThBCdo5jbaxv7B/n5+ezdu5epU6fi6+vbW3G1S6fT4enpSUVFhdSniA657/bnWec9ikd8ddy5dJG1wxlQbnp3N5tTi7h5nBfLvMtg/nwANh0u5P5PEqmobcDBToOrg7b5gQYD7nVV/OeW6YwZFWqFyIUQfa0zn9+dXrsnMDCQiy66yKoJihBdEenaNA252sqRDCzHi6rYnFqEosANK+6BBQvgm28AmD16CN/ccybRIZ7UG0yU1TQ0f9SbOaFx5a3VP1n5VQghbFGHpyAL0d9F+btBBaTLXYce9f7OTADOcdETeXif+uSKFXDRRaAohPm4sO4vM8goqcZ06sDt5i0cfXwld176GBsbPPin3nDaCtZCiMFNVkEWg4ZlGrIyuBrZ9abKugY+S8gC4Kaf3ju5YedO2LbN8qNGozDU343hQ9xPPl55hvOP7CCyNJcaO0c2bj3U1+ELIWycJCli0Igaq/boKXDyoEYv05B7wmcJ2VTXGxnubObMX9eDhwcsaqz3eeqp1g/cuRO2bkWxt+eygv0A/G/7kQ5ds1BXh8nUqVI6IUQ/JUmKGDS8Rg/Hq1ZtMJZ5VPp3dJfJZOa9nRkA3JT8PQrAnXfCP/4BGg189x3s39/ywc88o/73+uu5ND4CgB01ju3OAPp49wmmrPiZd7bJNHIhBgNJUsTg4ehIZHUJABmHM60cTP+3+UghmSU1uNspXLbuLXXZhXvvheHD4cor1Z0a2xQ0c/gwNK2G/tBDhC2cxxknkjArCl/uzmj1ejX1Bp79IRWAfVmtrxkmhBg4JEkRg0qkUgdA+olCK0fS/727PQOAqwv349Kgh+uug6aGjY88ov73k0/g+PHmBz73HJjNsHAhjBkD0dFcnvM7AF/sPE5rXRFW78iguKoegJLG/wohBjZJUsSgEumiNgvMLJZpyN1xrLCSrUeLUYAbPntJffKhh07uMHEizJsHJhM8++zJ5/Py4P331T83NXhUFC4YG4BTQx3Ha2F/dsVp19PVNfCfLSeTndLs/B5+RUIIWyRJihhUovzdAEivkcLL7mhqgT+nPo+w8vyToyKnevRR9b///S8UFKh/fuklqK+H6dNhxgzLru7zz+f8I+raYF/szTrteu9sTaeitgH3+hoASupMPfuChBA2SZIUMahEhg8BIEOR1ZC74+v9eQDctP4N9Yk/LHsBwMyZMHUq6PXw4oug08Hrr6vbmm4HNTnnHC47qk5Z/vr37GZr/ZRV11sKZR/aoo7ClGkcMcoMHyEGPElSxKAS2TgNudDRnZp6g5Wj6Z8q6xqoqFWncMeeOHjaqIiFosBf/6r++bXX1Ns+FRUwerSlbb6FszMzRgwhoLKE8noTmw6frBn6z6/HqdIbGKvLZVHiRgDMikJ5jdSlCDHQSZIiBhXPMSPwrlFrHjKO51k5mv6pQKcWH7vrq3FtqGt5FKXJggUwdqw6ivLPf6rPLV2qTlH+A+38i7jk4CYAvvg9B4DCyjpW71BHUZb88BYOJoNlNeuSaklShBjoJEkRg4urK5FVxQBkHMqwbiz9VF6FmqQE6Yph1Cg1EWmNRtP81k5QEFx7bcv7XnQRlx/8BVAXJiytrue1TWnUNZiYWJnLOWl74Mor8a0pB6CkSt8TL0cIYcMkSRGDTiRqw7CME0VWjqR/ym9MUgIri1sdFWlm0SIIC1P/fP/94OjY8n6hoYwM9SE67ygGk5n/bEnjw10nAHjo21dR7O1h5Up8a9SGfKWF0itFiIFOkhQx6DRNQ84oqrJyJP1Tfpk6wyawqqTtUZQm9vbwxRfq7Z777mt73/nzuaxxNOU/vx6n3mhianUu0zP3q31Yhg3Dp0GdPl5SIEmKEAOdJCli0In0UxcYzKiRaaxdkdeYHARWlYKfX8cOio+Hv/2t9VGUJvPnszBlC3bGk0XNS758EUVR1FEbwNfc2NCtVNf54IUQ/YokKWLQiQr3ByAdZytH0j8VlKojUIHUt3+rp7Pi4/F1c+Ts4wkAzNLnEZ+TApdcYunD4qtVk8vSipqevbYQwubYWTsAIfpaxOhIOFJEkYMb1XoDro7yz6Az8nRqwWqQQy/0KdFo4KKL+NtX7xA4xIs7P2nsVts0lRnwcVATI2mNL8TAJyMpYtDxHDMCn6ZpyFnFVo6m/ymoVUcyAtzse+cC8+cTWZ7Hk288REhZPpxzDkyZYtns66ImlSV1xtbOIIQYICRJEYOPtzeRlWqzsIxD6VYOpn/RG4yUGNW3jSDPXrpdNneuWmzb5JRRFABfD/W6pQ29c3khhO2QJEUMSpEmtZ4hI1NWQ+6MwsZbPQ6GerwCfHrnIh4eMGuW+udJk2DOnGabfbzV9ZdKzHKbToiBTpIUMShZpiEXVlo5kv7F0sitshglIKD3LvTwwzBunLrmj6I02+Tr7wnI+j1CDAbyVUQMSpG+LmCAjGqZhtwZ+Y0t8QMqSyAgtPcuNHcuJCe3uMl7iC8cO7l+j69bO9OahRD9loykiEEpKkzt75GGM2azfBvvqPwKtVtvUGUJ9OZIShvsA/xl/R4hBglJUsSgNGJsJI4NekrtXUiTzrMdlndqS3wrJSn4+8v6PUIMEpKkiEHJKTaGuNzDAOzYlWrlaPqPgnJ1JCWwynojKWqS0rh+T6nUFAkxkEmSIgYnd3emUw7Ajn0yDbmj8krUUaegzrTE72lubvjoG2/3FJRaJwYhRJ+QJEUMWtPHBAOws1KDSWaJdEhBU+Gs1gharXWCUJST6/eUyPo9QgxkkqSIQSv6wrNw09dQoXUi5USJtcOxeUaTmYJatctrkKt1Jwb6ahrX7ymX9XuEGMgkSRGDlt2kiZxRcASAHZv2WTka21dSpcdoBo3JiJ+Xi1Vj8XE4GZMQYuCSJEUMXhoN0zzUb+Q7UvOtHIzta5rZM6S6DLuAIVaNxddZbZtfUmuwahxCiN4lSYoY1KZPHgbAboMrDUZp7NaW5o3crDSzp5GvhxMg6/cIMdBJkiIGtdELzsGnpoIaO0cOJMksn7bkn9IS39pJio+XKyDr9wgx0EmSIgY1TWAg03QnAKlLaU/TSEqgLYyk+DWu36M4yPo9QgxgkqSIQW9aoHrrYMcJmc7alvwK20lSvAN8gZPr9wghBiZJUsSgN/3MaAD22vlQp5cih9bkWdbtsf7tHlm/R4jBodNJSnJyMvHx8Xh7e7N06dJ2F2draGhg6dKlhIeHExQUxLJlyzAYTq/ILy8vJygoiIyMjM6GJES3RM09k8CqEuq19vy++Xdrh2OzChpHUgKqSiAw0LrB+Pmdsn6PJClCDFSdSlL0ej0LFixg8uTJJCQkkJKSwurVq9s8Zvny5WzYsIGNGzfy3XffsXbtWpYvX37afkuXLiU/X6aBir6nODoy3VAMwPadKVaOxjaZzWbLFOSgqhLrtcRvcur6PRXV1o1FCNFrOpWkbNiwgYqKCp5//nmGDRvGihUreOedd9o85v3332f58uWMHTuWiRMnsmTJEtatW9dsn19//ZX169fj6+vb+VcgRA+YFuUDwI5i6bvREl2tgTqDOkU7wFEBOyvPqvH2xqdWTVJKCsqsG4sQotd0KknZv38/U6dOxcVF7TYZExNDSkrb3zyLi4sJDw+3/KzVatGesuaHXq/n9ttv56WXXsLNza3V8+j1enQ6XbOHED1l+nlTADjgEkBlSbl1g7FBeTq1HsW7pgInPx8rRwNoNPia1JGdkpIKKwcjhOgtnUpSdDodUVFRlp8VRUGr1VJW1vo3mUmTJllGToxGI2vWrGHu3LmW7StWrGDkyJFcddVVbV575cqVeHp6Wh5hYWGdCV2INoXEjiGyshCjRsueb7dZOxybk2+pRym1etFsk5Pr98jtHiEGqk4lKXZ2djg6OjZ7zsnJiZqa1hf5evXVV3nvvfc477zzGDlyJLt27eLOO+8E4NChQ7zxxhu8/vrr7V770UcfpaKiwvLIysrqTOhCtE1RmOagjhbs2J9h3VhskC01cmtiWb+nUtbvEWKg6lSS4uPjQ1FRUbPnKisrcXBwaPWYCRMmkJGRwQsvvICnpyeLFy8mKioKs9nMbbfdxpNPPklwcHC713Z0dMTDw6PZQ4ieNH1cCAA7qu2tHIntsaVGbk18ndW6GFm/R4iBq1NJSnx8PDt37rT8nJ6ejl6vx8en7XvUWq2WmpoaUlNTeeKJJwA4ceIE27ZtY+nSpXh5eeHl5cWJEyeIiYnhww8/7PwrEaKbpl44A4AUr1DKUo5YORrbcrKRm+2MpPi6N67fUy8dZ4UYqDqVpMycOROdTse7774LqPUkc+bMQavVUl5ejtFobPXYZcuWsWTJEsuoSUhICOnp6SQmJloewcHBfPfddyxcuLAbL0mIrvEP9mNUtTpS+Nt3O6wcjW2xTD+utIEeKY18PNUC/hKTtp09hRD9VafmEdrZ2fH222+zaNEili5dikajYfPmzQB4e3uzb98+YmNjTztuy5YtJCYm8tlnnzU7V2Rk5GnnDw0NbXOWjxC9aZoXpDbAjqOFXGDtYGxIge6URm62MpLi5wl5J9fv0WoUa4ckhOhhnW52sHDhQtLS0ti7dy9Tp0619DZpq/PsrFmzyMvLa/fc0m1WWNv0+BGs3lHOdrzAYLB+PxAbkWeDhbPeAT6Qd3L9Hl83x/YPEkL0K11auycwMJCLLrpImq+JAeeMc+MAOO4dQnnyYStHYxtq641U1KprGgXYUOGs/ZCT6/eUyvo9QgxIssCgEKfwdHUkpK4cgCMJh6wbjI1omtnjUl+Lh74a/P2tHFEjf3/L+j3Fsn6PEAOSJClC/MEojfqhnHos18qR2IaTM3tKUHx9wd5Gpmifun5PZZ2VgxFC9AZJUoT4g5F+zgAcKam1ciS2Ib+xJb4t9UgBwNcXnxq1JX5pkazfI8RAJEmKEH8walgQAKkGKcQEyK9QO7oG2tDMHgDs7fExqiMoxcWyfo8QA5EkKUL8wchJowE44hGIubjYytFYX35F00hKsc30SGnip1G7zZaWyfo9QgxEkqQI8QfDIoegNRkpd/agaM9+a4djdXkVttcSv4lPY3mM1KQIMTBJkiLEHzjZa4lsUKe2pu4/ZuVorK9AZ3st8Zv4OKl9bIprGqwciRCiN0iSIkQLRjmbAEg9Ibd7bLGRWxM/N3VxU1m/R4iBSZIUIVowMkhdZfuIrvX1qAaDBqOJoiq1cNaWWuI38fFyBaDUJG9lQgxE8i9biBaMGhMBQKrWHRoG762Eoko9ZjPYmQz4VVfYXJLi66smk6Wo6/cIIQYWSVKEaMHI6KEAHPUNw3R48LbHz7csLFiKBrPNJSneQ7yBk+v3CCEGFklShGhBhJ8bDiYDNQ7OZCckWzscq7F0m9U11uYMGWLFaE4n6/cIMbBJkiJEC+y0Goab1d4bqYdOWDka67EkKVUl4OMDDg5WjugP/P3xbew6K+v3CDHwSJIiRCtGeapNOI7k66wcifXk62y3RwrQLEkprdZ37tjKStB38hghRJ+SJEWIVoyM8AMgtW7w/jPJt+FGbgD4+59cv6e4E8nkiRMUjhxP7exzB3VhtBC2bvC++wrRjlHRwwA44hYABQVWjsY6TiYpttcjBQBnZ3waagAoLirv8GGZ//2Qmdc8z4Xjb6DkxVd7KTghRHdJkiJEK5pGUtJ8Q2nYl2jdYKwkr3EFZFts5NbET1FHQkrLqzp2gNnMZ/vyqLN3It0nhNuSTdSlZ/ZihEKIrpIkRYhWhHg542pqoEFrT8a+wTcN2Ww2U9C4AnKArd7uAXzUzviU6jq2fo/pt9/4MjgWAI3ZxN6gUTz07/WYpM+KEDZHkhQhWqEoCiPt1BkjqemD73ZPpd5AvVFdHsC/ptx2kxRnLdDx9Xt+++g7cjyH4G6q553ZAdgbG/jGYyjPvfZtb4YphOgCSVKEaMMofxcAjpQMvlV2q+oMANibDDga6m02SfFzbVy/R29qf+f6er7IVpOZ+aGOzD5/CisVdRHJV7MVPt2e1mtxik746COIiIDffrN2JMLKJEkRog0jhwcDkIrLoJuuWq1XkxTXhjoUgMBAq8bTGh9PNZEsNbb/dlbzzQY2RMYBcPn8KQBcsewO7k1SR1Ee+zqF7cdkUUmrqqqCe++FEyfgpZesHY2wMklShGjDqNFhABzxDYdDh6wcTd+qbExS3OrUpna2OpJycv0e+3bX79n49Q5qHJyJMNcwOcpXfdLNjQduPY+LD27GgIY73tvD0YLK3g5btGbVKihuTBS/+06miA9ykqQI0YaRgeoHYIZ3EHX79ls5mr7VNJLiplen+NpaS/wmHV6/p6yM/9V5AnBZTCCKolg2KZdczDOao8RnHaSywcTtH+zFbJZC2j5XVgb//rf6Z40GKipg61brxiSsSpIUIdrg5+aAj7kes6LhWPJxa4fTp5pqUtzqa8DLCxwdrRtQKzq6fk/uh1+wPTwagMsumNx8o6LguOpF3tzwLA6Geo4XVZNeXN1rMYtWPP+8mpiMGwfXX68+t369dWMSViVJihBtUBSFkWrJA4ezSq0bTB+raqpJqa+12Vs9QIfX7/lq62HMioYpdtWE+bicvkNkJN6PLCE27wgACcdLeiVc0YqiInjxRfXP//gHXHKJ+uf160FGtQYtSVKEaMeoEC8AjlSZBtWbZdWpt3tsOUnx8ztl/Z6WkxRzWhr/c1M7CF8+a0zr53rgAeIK1dk+e/bJTJ8+9cwzatHspElw6aUwd646epeeDikp1o5OWIkkKUK0Y2Rj8Wyq2xDIzbVyNH3HUpPSD0ZSLOv3tNJ19sD7X3LMLxxHk4ELzhrd+rkcHIj3VReWTMiq6PFQRStyc+GVV9Q/P/kkZTUN/JhZhXHOXPU5ueUzaEmSIkQ7Rof5AHDELwIOHLByNH2nsr8kKR4e+OjVmpTiwvLTt5vN/O9gEQDzvI14ONm3ebpJcSNRzCbSjQ4UVQ6uaedWs2IF1NXB9OnsHXMGF6zayp/fT+CZGdeq2yVJGbQkSRGiHSMC3AHI8/CnYl+SlaPpO5Y+KfU1NtsjBQBFwY/G9XvKTh9Jqd/5G+uDJwBw2YVx7Z7O89xZjCpS1/LZm1bUg4GKFmVmwptvYgZW3/YEV735G/mNSxysrvKg0NUbdu0atIt8DnaSpAjRDg8ne4IVtdbh6JFsK0fTd5pm97jrbXwkBfBpHBz5KbuG1dvTm01F3vTJj5S5eDLEWMuZ40PbP9mECcQXqfUoe/ak9ka44lT//CfVaLnv1md54lA9BpOZi6KDiA3zQm808+rFd6u1YN/KsgWDkSQpQnTASG+19XpqQQdX2h0AqvRGoHEkxcaTlCnmCpzr68jTwxNfpzBlxc/c89E+th/M4fPGL+CXRLlip+3AW55GQ5yPuh5QQsbgmtHV544e5dj6H7nkhudY7zsaO43C4/PH8so1E3l43igAPgyLJ9vDX275DFKSpAjRAaMi1UZmR4yOUFtr5Wj6Rr8pnAXGucHO12/iCfdCxgR5UG8w8fX+XK5dk8iPkWpPlMsvmdbh88XHDgUg2eBETb2hV2IWsOP5/3Lxtc9y1C+CIe6OfHTbVG45MwpFUZg+3I/pw3xpQMMr06+GH34YNP/2xEmSpAjRASOHqTUZqb7hcHxwNHWr0qt1HjY/BRnAzw+vuipuKk/hO+fDfP39U1z3+7e416kjX7HuMCrYq8OnCz5vFiEVhRgVDYlSl9JrXqgPotrRhTO84Nt7zyI+0qfZ9iXnjQTgs+g5pDt5w88/WyFKYU2SpAjRAaMa2+On+kdgTs+wbjB9pLq2MUmprwV/fytH046m+J59FuX224hO3MaTSV+yO7KQ1dfG8ua953bufGPHEles1qXs3pHcw8H2f9nLnybtnkfAaOzyORryCzjgpdYI/evKifi7n97ReHKED7NH+WPUaFk1YxF8/XWXryf6J0lShOiA4UPcUMxmylw8KT5+wtrh9InKOjVJcTXVg7OzlaNpx6kjPQEB8MILcPw4zvffw9nRIQxxd+rc+RSFOC/17TEhTVZFPlVDQSGXFQZxkcMUstd/3+XzpG5JQG/viHtDLUOjglrdb8l5am3KurGzOPLrXjCZunxN0f90KklJTk4mPj4eb29vli5d2u4CXA0NDSxdupTw8HCCgoJYtmwZBsPJ+7vLly/Hx8cHR0dHLr30UiorZeVRYZuc7LUEofbMyMoeHB9a1fXqt2R3Rzs4ZTE+m3TJJXDNNfDcc+rtuPvv73ZiFR8TAcDvDU4YjPLB2OTAT7sodPelzt6JNd8ldvk8+5IyAIg1VaDRtP77NT7EkwvGDsGsaHhh9Hmwd2+Xryn6nw4nKXq9ngULFjB58mQSEhJISUlh9erVbR6zfPlyNmzYwMaNG/nuu+9Yu3Yty5cvB2Dt2rWsXbuWjRs3cvDgQQ4dOsRTTz3VrRcjRG8KcVCT8pyigZ9MG01magzq63Vtp/mZTRgyBNauhQcfBJcW1uXpgpHzzsK9rooarQOHMqQupcmOpJMjiR+7DKX2RE6XzpNYqBbBxvq3P8r1wLzRKGYzG0bNIHndT126nuifOpykbNiwgYqKCp5//nmGDRvGihUreOedd9o85v3332f58uWMHTuWiRMnsmTJEtatWwdAVlYW7733HlOmTGH48OFcddVV7Nu3r3uvRoheFOqp3jPPrmywciS9r/qUGS1uLra5+nFv04wYQVxJOgB7fk20bjA2ZEfFyVGPCmd3vvpvF+pEzGYSFbXOa2J0ZLu7jwxw52IvtffNc7kOnb+e6Lc6nKTs37+fqVOn4tL4LSUmJoaUdhZ9Ki4uJjw83PKzVqtFq1X7D/z1r39l2rSTUwJTU1MZMWJEq+fS6/XodLpmDyH6UkiAJwA5DQO/lKupkZu9sQFHDzcrR2MlikKcuzqalJCab+VgbENddS17XdX6kWsU9f/J6nwt5k4W0FYcTCXNOwSACWdO6NAx9195BlqTkU1DRrN316FOXU/0Xx1+t9XpdERFRVl+VhQFrVZLWVlZq8dMmjTJMnJiNBpZs2YNc+fOPW2/I0eO8OWXX3Lbbbe1eq6VK1fi6elpeYSFhXU0dCF6RGi42isl28EDqqutHE3vqj51BWRPTytHYz1TxqnvM3v0Tu3W4A0Gv29KoN7OgcDqUh5ZegUuDXWkegWz87MfO3WeA9v2AxBeW4avd8eS4MjhIVxedBCAz39I7NT1RP/V4STFzs4OR8fmw75OTk7U1NS0esyrr77Ke++9x3nnncfIkSPZtWsXd955Z7N9TCYTN998M7feeivjxo1r9VyPPvooFRUVlkdWVlZHQxeiR4QE+wKQ4+kPJwb2DJ9Ky7o9tYM6SYk+fwYOhgaKHN04kSV1KTt+V29/TW8oxtPHk8tR2/m+uz29U+dJPJIHQKxDXaeOmx2p3iJKKhv4t1yFqsNJio+PD0VFzf+RVlZW4uDQ+v3BCRMmkJGRwQsvvICnpyeLFy9uNhoD8M9//pPS0lL+/e9/t3l9R0dHPDw8mj2E6Euh3uqtzmyPAMzpnXtT7m+adZsdxEmK07AoYsrUhHTPzwlWjsb6dpSot3WmBav/Fm68dCoAP7mEknWk44l7ok4dlYoN8+7U9cc31q+k2nmiN3S9R4voPzqcpMTHx7Nz507Lz+np6ej1enx8fNo4Sq1DqampITU1lSeeeKLZtq+//prnn3+eL774wlLrIoStCvJUZyHUOjhRdnxgj+Q11aS41Q/u2z0Aca7q/4uEQ12bxTJQVNU1sN9RHU2cdsZoAIafOYmzStMwKxreX7upQ+cx19WR6KL2tYmNG9WpGEKnTMCrVkeDxo4jJ0o6dazonzqcpMycOROdTse7774LwIoVK5gzZw5arZby8nKMbRROLVu2jCVLlhAcHGx57tChQyxatIiXX36ZsLAwqqqq2rx1JIS1OdlrGWJWh6dzsgqtHE3vqrLUpAzukRSA+DFqgefu2n4wFbsX7dl1CKNGS0R5HqEzp1iev3m8+kX140pXqmvrWzvcInvnPkpcPLE3GhgbN7pTMSghIUSXZAKQ9PuRTh0r+qdO1aS8/fbb3H333fj5+bFu3TqefvppALy9vUlKSmrxuC1btpCYmMjDDz/c7Pk333yT6upqbrzxRtzd3XF3d2fs2LHdeClC9L6mXinZA7xXSpWlJkVGUibPU2chHnf1pyRnYCenbdm++ygA02vymjXKm3XzJUSW51Hp4MyXn25u9zy/71Zn5ozVl+DkYNe5IBSF8Rr1y2zSMZlxNRh0ai7lwoULSUtL47333uPQoUOWpMJsNhMbG9viMbNmzSIvLw83t+YV3C+88AJms7nZIyMjo0svQoi+Euqh1mDl6PRWjqR3NdWkuA/ymhQAr6HhjNSphZ4JP/xm5WisZ0eh+js/bUjzOkSNmxs3Oqq3XlbvL2p3FlRiVjmgLvrYFeN91duuyaVSPDsYdLrhQ2BgIBdddBG+vr69EY8QNi3EX/3Azq4f2L1SZHZPc3GO6m2MhOSBXYvUmrLqelK06u/BtLjhp22/4to5uOprOObgxbaEtDbPlahXZ4nGjgjsUizRo9Tbb6m4UG+Q5QoGuoH9TitED7P0SrF3g9paK0fTe6rldk8z8aPUD9Td1VorR2Idvx3MBmBkUSb+s6aftt19ymSuLFRv+a/+uvW1deqLijnoqdYmxp4Z06VYwiaPxbO2knqNHUcKpKnnQCdJihCdEBKsFglmewZAZqaVo+k9TbN73KVwFoC4OWqh6EH3IGqzc60cTd/bsSsVgOnl6RDU8orFN06PBOAXvQvHClqu2Tq0WW0G56WvJnJYSJdiUcaNY3yhOlqTfCi7S+cQ/YckKUJ0QpiPOlU+x8N/YCcpMpLSTOiwEILqKjBo7Uhcv9na4fS5Hblqh+VpPq2PJEXdcCVzj+/BrGhY9f7mFvdJPKD2F4o1VaB0dWVtFxfG60sBSDo0OG+/DSaSpAjRCcFe6qyGSic3KtIGcJJSoxZJDvZmbk0URSHOWS3UTNh/3MrR9K0CXR1pZmcUs4mpEyJb39HdnQeGq9O0vy7RcKiFPiaJRY0rHw9pf+XjtkR7qh9dyfkDe3kKIUmKEJ3i4mCHr1n9AM85MXCno1bXqIWibgZ9s+mmg1l8dAQAeyo10DB4ZpbsPKp2Gh+fn4bnzNPrUU419tF7mX98FwDP//fn5hvNZhIVNeGN7cDKx20ZP1StDTtkcKDBKMWzA5kkKUJ0Uoi9+qaYXVhh5Uh6T1Wd+iHsZq+Brg7LDzBxM9XVen8PGIFxxw4rR9N3diQcA2B6/mFoY301ANzduX9mJBqTkR/rXNl/8GSr/PLDaaR7qgXIsWdN7FZMERNG415XRb2i5Ugr9S9iYJAkRYhOCnUf+L1SqurVDtKujp1stjWAjQr2wt1UT5WjC4c2/GrtcPqE2Wxm+wl1Bs00dxNo25/dNPyOG7g0JxGAZ9/bbHk+ces+AKJqSvDq4MrHrVEmxDC+oLF4NrO0W+cStk2SFCE6KcRf7UI1kHulVDWoDbncnQd3K/hTaTUKkzzVUaWEg4NjVklWaS05RjvsjAamRId37CA7O+770xnYGQ1s1fiya+sBoOsrH7coPJzoMrVoNjllYK9IPtgN3HdZIXpJaJh6PzxH6wJ1PfCGa2OMJjO1jbf5XV0crRuMjYmfMBSAPRovODHwPxx3pBUDMDE3FZcZUzt8XPjF8/hT2WEAnvt8D2az+eTKx6Fe3Q9MURjvrP6SJmWXt75fQQHMmwcffND9awqrkCRFiE4KCVG7LWd7BrT9QZWZCevXQzttwm1N0/RjADe37s3CGGjixqq9PRJCx2H+boOVo+l9O5LVEaNpWQfgjDM6dew9t52Pg6Ge3c6BbP3sRxKdG1c+ju/cooKtiQ5Vi3AP1SgYWiuefe01+OEHWLGiR64p+p4kKUJ0UuipvVLaWG/qx7v/ziOv/0TFZ1/2UWQ9o6nbrL2xAUePLi6wMkBNCPXCHhMF7r5k/7TV2uH0KrPZzI7jar3HdLtqcO/c70LQlAlcZ1STnMe25FLu7I6DoZ4xU9opvu2giPHDcdPXoFe0HC2sOn0Hs5mKL9bxyPn3sFnvCjU1PXJd0bckSRGik0Iae6WUuXhSnd7KSEpZGU8Gz+CTCfP4x8/pfRhd9zWNpLjppZHbHzk7aBnvq94C23O8eEDe7mtyOL+SYoOCU0MdE8eGdekcd957Gc4NdWS7+wMwTl+Cg33PLC2gmRDDuMbi2aScFmbaJSbyYuAUPpkwj6dm3QhJST1yXdG3JEkRopPcnezxNKt9RHIyW14uvvDHLWR6q2uUfOE9mi27jvRZfN1VJYsLtmnKOPUDe8+Q4fDrwJ3lszlV7Y8yPfMAjtM7Xo9yKv+oEG5yO7m+TldXPm7R+PFEF6jTo5OPnf7vMPej/7E29kIAjviFU703sQcvLvqKJClCdEGIXWOvlIKWe6Uk7DzY7OfH1qVYbqPYuqZ1e6TbbMviItX1mxJCx8J331k5mt6z6aC6RtHZx/fCjBldPs/t912Be73aabarIzItcnMj2qz2SElKL2q+zWTi5eMN1Nups9NMGi0HZBZQvyRJihBdEOquvvnlVLQ83L87V71HfkXxQUIqCsgx2fPsD6l9Fl93NCVTbrJuT4smR3gDcNQvgrIfN1k5mt6hq2tgb5aagJ+trYDwDk4/boGXjzsvXTySmwMMnH/VnJ4KEYDxQ9T6sEMVxmbFs5k/b+ezqGkARNmrv8/7CgbuquUDmSQpQnRBiJ/ajCpb30I31vx8EpzVzpqzrpzDyp9eB2D19gz2Zpb1WYxdVWmpSZGRlJb4uDow3FetS0qod4KjR60cUc/bllqIEYWhJVmE//n6bncdnj0rhmUPXNxj9ShNokZH4qavoQ4NaUUn1/FZ9f0hDFo7zq7LZVGs+m8x0ewGhv4xmilOkiRFiC4IDVULAbO1LqBv3nm26qdNpAyJAiBu6hhmxoRzWdLPmIG/fnEAvcHY1+F2SrWsgNyu+GF+gDoVucVbPiYTLFum9uio6H/LJ2z+6XcAzs49CNddZ+VoWqeZEMPYPxTPHsur4CtNEABLpgYTGzsMgMSA4ZhT+8dopjhJkhQhuiAkVP2QyvHwh6zmy8Xv25GESaMl1FxLkKcz3Hgjj//yNn61Oo4WVvHapjRrhNxhTTUp7lKT0qq4CLUuZU9LdSl1dXD11fDPf6o9OtautUKEXWc2m9mco07XnR0TCi4uVo6oDRMmnGyPn6WOUr7w8U5MGg3z0vcQfdlcosO80ZpNFLr7krd7vzWjFV0gSYoQXRDqrb5xZ3sEnNYrZU+WOpshPqBx9eDzz8fbzZEnfngNgNc2HyM133YXRauql9k97YlvLJ5NChxO3dYdUNXYp6OsTB09+eyzkzt//rkVIuy6lE17KHRww7mhjil3LLJ2OG2LjCS6XO3FkpRWwMHcCr4tMKKYTTzgUwkODjg7aBllUv9+ElMGx3IGA4kkKUJ0Qai3moAUu3lTdzzj5IaMDBJc1anH8ZNHqM85OMA113DR4W3MqcmiwWjmkS8OYDTZZiday+we6ZPSqjAfZ4a4O9KgtWe/bwT88os6onbWWeq0ZA8PePttdectW6CoqO0T9iSdDhYtgo0bu3T45i9+AWBGQzGOEV0vmO0TGg3RXuoimCkldTy7QW3Fv+DQr4y+8iLLbhO91VqYxCIpnu1vJEkRogs8ne1xMzcAkHOiwPJ8wy+b2Bc8CoD40UEnD7jhBhTgyQ//ibujlsSscr4/2HKPFWurrlZnLMkU5NYpimIZTUkIHQuvvgrTpsHBgxAUpCYqt9wCkyap9SlffdV3wX32GXz8Mdx+u3rtzsjPZ7NO/UCfNb1n2tf3tqgRIbjU11JrUth0tBiNycj9h3+AWbMs+8SOUv8tJprd+t0yFYOdJClCdIGiKITYqQWwOfnllucPbkuk1sEJLxoY5n/KcvSTJsG4cQSW5LLAUb0dlNxSl0wbUNWUpDTU2nY9gpXFRapTkfeEjlNrT3JyYMwY2LkTJkxQd7riCvW/fXnLp6lG6sQJ2Ly5U4dWvP4WvwepSfbZ58X3cGC9QzthAuMKjlt+vjz5F4aePwu0J2cSTZwyFoAk30gMJ7JOO0e/VFcHX3wBlbZ767gnSJIiRBeFuqrDzNnljUPIZjMJJ8oBiPN3QqM5ZdqmosANNwAwLHEnAMdPmTJpS6pq1G66rnZKt6eeDmRNIyl7Q8diVDRqw7Nt2yAi4uROl1+u/veXX6C0tE/iys0r5bybX+W9SfNh9eqOH1hXx9aNuzBqtAx3MhHm008S1JgYxjd2nrU3Grh3+0dq4fIphob44N5QS62DE6k7Eq0QZC944w01CT7//NNmGA4kkqQI0UVNvVJymvq5HTnCHvdQAOImRJ1+wHXXgUbD0L3qwnTHi1tYFM0GVNWpt7Hc7SVBacvoQHfcHO2odHQh9dF/wo8/go9P851GjoToaLU/x/r1fRLXz9WOHPGPYMXZi8nf+EvHv2l//DGb/YYDMHvy0F6MsIeNH8+8I2rif9uuLwjzcoKpzdv4azQKE0zqyOWAKZ7dtUv9744dcO+91o2lF0mSIkQXhYao05CzNc5QX4/5p5/V+gQgfoT/6QcEB8OcOQwrUd8kM0pqbLJ4trpevY3l6mhn5Uhsm51Ww8RwLwAS5l8Dzs6WbVV6A5/uyeLtrccxXN63t3xyG79U6+0deTV2QceuazZjenEVW6ImA3D2mIBejLCHeXoyVVtFyvOX89DWNXDVVaA5/aMt1kftEr2veGCMOpQeSeeFGddw1DcM3nxTHVnpQQajibtf+4V/rz9AXYP1ejtJkiJEF4WEqYlIjoc/ZGeTvi2BElcvHDAxPqSVgtMbbiBEV4SDsYF6g4ncctubbVDZoBZbujnZWzkS29d0y2dPRhlms5m9maU8/Pl+pvzrJx7+4gBPfnuIj6LPU3f+8cc+aeyWY3a0/PnjCfPI+uh/7R+0ZQsp+ZUUufngYq+x1Nv0GxMm4NKgRwF1ZlMLYkeps+4S6clVDq3EYGC1+yhWnXkNC295ma/Gng333ANbt/bYJXIy8vnmRC1vbzmGQ731VvuWJEWILrL0SvEMgOPHSchQm0nF+jrgaNdK++9LL0Xr6kJkqbp4W1qR7d3yqW780uTm4tj2jsLyYf7rkSLmvvArl7++k08TsqmpN+Lj6gDAy4eqqBs7Hurr4Ztvejeg2lpynTwAcLFTaNDa85LdMEhrp4Hgiy+yeag6ijJjhH/rv7+2KiZG/e/IkRAb2+IusdOjAUjzCECX34dTwnvDsWMkDlE76dYqdty/4CGWnX0r+j9dfVpzya7KSFGLkSOqitG4uvbIObtCkhQhuiiksVdKgZsP9eu/Zo+3WjAZN66NlV5dXGD+fKJKcwDbK541GE3UmtW3BTc3JytHY/tiw7yw0yhU1DZwrLAKZ3stl08K5dPbp7Hz0XMI8XKmsFLPBxffoR7Q27d8cnPJ9VBH+B46X51C/MX4czi++tPWj9m7F9avZ9PQOADOHtXCrUpbd8MN6gy6FStaLfb2Cw0gtKoYs6LhwLb+3XnWnHyQ5EC1fmjBBHWE6P3J87lq7oPkXn0j1HZ/hDYjQ22tEGmy7nuUJClCdJGvqwNOZiNmRUPe+h9O1qMM9Wv7wEmTGNqUpNhY8WxTPQqAq7v1vj31Fy4OdjwwdyRnDvdjxaXR7P6/c3nuTxOYEuWDo52W+85VG/q9Zj+UansntcFaVe/9nRuyssl38wVgfkww53oYMGm0vHhM33LPlJoauPZayh1d2ReiJjVnjxrSa/H1muHD1WSraTZVKyY2Fc8eyumLqHpNbvIRSl08sTOb+PcVMbx7UzyejloSg0dz0eRb2XrP493uB5NeqLZKiHSybt2cJClCdJGiKIRo1ZkwiS4BpPuEoGBmUng79/NjYhhaqhbP2tpISlXj4oIOhgYcPQfAvfs+8JfZw/ng1jO45oxw3P9Qx3PZpBAifV0o1Zt497zFam+LlhYk7CEFmXmYNFrsTUb83Bx5cNE0AL4On8zhbzeffsAjj0BqKr9OOheTomFkgBshXs6n7zdAxPqqt+ASS/p38WxSZgkAI+zqcbLXMnv0EL65bybRHhrKXDy5wXcWn/77/W5dI6NK/cIS6W3dEVVJUoTohqZeKV+NOxuAUZ72eLq0U3A6YcLJkZRCGxtJkRWQe5SdVsMDc0cC8J/x86hwdO3VWz65ueqHV5CxBo1GYVzUEC6qz8GsaHj+52PNd/7+e3jlFQA2X3k7ALP74yhKJ8SODgEgUfHA3I87zybr1Nij/U4mEGE+Lny29DyucirHrGj4V54zuoqufwnKNKk1aZGhvt0LtpskSRGiG0J81Vsiv0ZNAiBudHD7BwUEMExRq+XzK/WWxKA1uroG3tuRQXFV73/7axpJkZb4PWdBTDCjAtypxI63p1wK336r3mbpBblFak+UYLuTt+0eOH8sGpORH5xCOHAkT32ypAQWLwag9J4H+bVKTbZn9cd6lE4YNz0Ge2MDxY7uZJ/SKbpfaWgg2U79txk9MqjZJid7LSsevZIR5blUOLryztsbunQJg9FElqN6jaiRbdTY9QFJUoTohqZeKUaNOhsiPsqnrd1VioLX6GH41Kj3x9OL2/62887WdP6+/iD/2dLODI0e0LS4oKyA3HM0GsUymvLf+EsoMdupoxi9IKdS7RYc7HLyrX343Blckv07AM998huYzRhvv4MtTkHcdf2/OMPtXIqr6nF3siMuogO/v/2YU2Q4Y0rV2S+Jvx20cjRdYz56lOQharO98eNPbxqpdXTgwWD19+CdPIWyLny5yc4vw6DR4tigJ2D8yO4F3E2SpAjRDSGhzb95NvXNaNepdSntJCl7MtR26hklvfPt+1RNozrusgJyj5o3LoDxIR5U2zvxnzMu77VbPk2N3JrVlSgK9411x85oYEu1A489/QVnDbmQG//0D74LnkCDyUxMqCf/uX4yDnYD/CNBUYg1qwWh+w73z+LZ/MQUil290ZpMjAn2aHGfeXddxbjC41TZOfHGh792+hrpB9Xpx5G6QjS+crtHiH4r9JT1TULc7AnuaNFhTMzJupQ2eqUYTWYOZKsjLvkVvd9QqVIvIym9QVEUlpynLtz33qT5FP68tVfWW8k1q4WhwUOa/91F3HQVVyb9BMCH5c7kegzBSzFw0/RINtx3FuvvPpPpw9qZlTZAxPqptRaJpQ1WjqRrkhqTqxFGHU72Lfez0fj5scS5EID3jtVQWNm5947M4+ptwUhDpdXX75IkRYhuCPU+mZTEDe/E/fwJE07pldJ6kpJWVGWpE8nrgySl2lKTIiMpPe3skf7ERXijt3fkleiL1NqUnmQykeugzsgKDvtDAWxoKPdrs4nJO8JZ6b/z8uGv+O3vF/DEwnGMCWr52/hAFTtGXV8rWXGn3vCHadlmM3z8sboejo1KLlBHXse7tb3f7LuuZmLOYeo0dry2bl+nrpFR0Dj92NF67fCbdDpJSU5OJj4+Hm9vb5YuXdpuhXRDQwNLly4lPDycoKAgli1bhsFwslDw888/JyIiguDgYD766KPOvwIhrMjfzREHrfrPKK6jt3oAxoxhaLn6beV4bnmruyWeOLmtpFp/+ptqD2uqSZHC2Z536mjKR7HzyH717Z69QGEhOe7qaEhI1OkF3AHXXsn69x9kzXfPsOD5R3FycujZ6/cTUVOi8aytpF5jx+Gc8pMbysvh0kvVtvoXXmizKwsn1at/b9Fhbbc6UMaN46GaFAA+TCru1BIc6ZXqKFOkl/UbOnYqSdHr9SxYsIDJkyeTkJBASkoKq9tZCnz58uVs2LCBjRs38t1337F27VqWL18OqAnPtddey+OPP87333/PsmXLSE1N7fKLEaKvaTQKE8O9cLTTMHNEJ4bLnZwY5qm+2aSX1raa7O/LKrP82Wym08O2nVVVpb6RuUlNSq+YNsyX6aFuNGjtWW0Ohu3be+zcuowsKp3Ur9dBfi30uLn6anj2WdiwAYb2o1WOe5gyejSxBep07MR9R9Un9++HuDhYt079uaKiR9fB6SlmvZ4kt0AAxse0sNL6H0y/5XKmZh6gXtHw8vcpHb5OplF9b4oMtn4hdaeSlA0bNlBRUcHzzz/PsGHDWLFiBe+8806bx7z//vssX76csWPHMnHiRJYsWcK6xl+Et99+m9mzZ3PrrbcSHR3N3XffzZo1a7r+aoSwgjevj+OnB2cR4du5Dq3hQ4PRmoxUG6FA1/K3tn2njKQAFOj6JklxbagDt3bGk0WX/HnuGAA+iZlL9bMv9Nh589LVkTmv+pqWV7DWaGDJEjjzzB67Zr+k1RKLejtjfVIB777wCe/e/g/e9RrLu3Nu5N2rHyTFP6rnb8f1gMIDhyl29UZjMjF2woh291fOP5+HMrcA8Nm+PDJL2u+b0mA0kWWvJrlRI0K6F3AP6FSSsn//fqZOnYqLi1osGBMTQ0pK29lZcXEx4eHhlp+1Wi1ardZyvnPOOceybcqUKezdu7fF8+j1enQ6XbOHELbA08WesFMKaDvKYUI0YeXq+hgt1aVU6w0cKVD7XoSV5wO9X5dSXa2e301jsnrB3EA1a4Q/UR72VDq58b+MGjh8uEfOm5unNnILNvX+LLD+blJj8WxCtZblBW4sP/tmls+5neWTr2R5xDlcd/WT1H+30cpRni5pnzoCNKKmGOeWEtE/0miIu/5izk5LwIDCqp+OtHtIdlElRo0Wp4Y6Asa1nwj1tk4lKTqdjqiok0NMiqKg1WopKytr9ZhJkyZZRk6MRiNr1qxh7ty5LZ7Pw8OD3NzcFs+zcuVKPD09LY+wMOs2mBGi29qZhpyUU4HJDIGVxUzIU99cenuGT1WNOqLj1s8Wwe1PNBqFG2epb/6r4xZifvbZHjlvTuM6UMHatpsDCjhzfCh37fyUBSlbWHBoCwu0pSyICWLBhGA8nOwodfFkR4MLHD1q7VCbaWqHP96uEwsI3nADSxK/AuDLfTkcbfzi05qM1EwAIssLUEJDuxRnT+pUkmJnZ4ejY/Pl252cnKhpo3viq6++ynvvvcd5553HyJEj2bVrF3feeWeL52vrXI8++igVFRWWR1YPLUcthNWcOg05v+K0zYlZ5QDE5qYSVFkMQH5F91c3bYulmZuDZCm96fLJobjZQZpvGFu3HIBWvpx1Rm7jLcMQlw58wx7ktDPP4uGta3h5+zu8fNe5vPyv63n5mkm8vGiiZVXh70dOt7lbPsk6dbbNqe3w2+XqSvRl5zEvdQdmFF7ZdKzN3TPS1N/FyIYK9RahlXUqAh8fH4qKipo9V1lZiYND61XiEyZMICMjgxdeeAFPT08WL15sGT354/naOpejoyMeHh7NHkL0ayEhDK1TRyGPZxaetrlpZk9s7hECKtVvUPmFpyczPalpurO7JCm9yt3JniumRAKwesIF8NJL3T5njl69PRfsPXAXCOwxY8dCYiKkpsK8ec02XTBebTX/w4ipGL/tvcUguyJJqxazjx/RgeU3TvWXv3DbXvWOxuaUfEym1mflZuSVAxBpbxsjcp1KUuLj49m5c6fl5/T0dPR6PT4+bVcAa7VaampqSE1N5Yknnmj1fPv27SMkxPqFOkL0CUUhyk8ttj1efPoIYuIJtdNsbF4qQXWNDd1KendBwmqD+ubl6tzOIomi226aHomCmV+GTyH9wy+hm3V2ubTcyE20IiYG/E6fkXfGUB+8HDWUuHqxO60IKtu+PdJXCot1FDp5ojEZGTtlTOcODg8nZtp4nOvrqKg3cayN3kzpusbpx56Ore7TlzqVpMycOROdTse7774LwIoVK5gzZw5arZby8nKMxtYbvyxbtowlS5YQHHwyA7z88sv5+OOPSUpKoqqqipdeeol5f8hqhRjIhkUFAJBdr0FvOPnvJ7+ijvzKejQmI9EO9QQGqV8E8hvXZuktlY1fntxcbOMNaiCL9HO1rDr8/shZ8Oab3Tpfq43cRKfYazXMGd94y2foFPjxRytHpEpOUAush5Xn4RIZ3s7ep7O//TYm5qotPvakl7a6X0aDerswMsir80H2gk7XpLz99tvcfffd+Pn5sW7dOp5++mkAvL29SUpKavG4LVu2kJiYyMMPP9zs+QkTJnDfffcRFxdHSEgIWq2Wu+66q4svRYj+xz96NG76GkyKQuYpa/MkNvZHGVWUievZZxHoq34AFdTT5lBtd1Wb1VsGbq7Wb+I0GNw0Q731/Vn0XKpefg3qu5aEGip05LuqiWzIcJlU0F0XjFd7kWwcNR2TjdSlJB1Wi+yjG0q7NvNu2jTi8tREJyH5RIu71BtMZNurrQeihtvGXY1OV8UsXLiQtLQ03nvvPQ4dOsTYsWMBMJvNxMbGtnjMrFmzyMvLw62Fvgv/+te/2LdvH59//jk7duzA2Vnup4rBQ5lwygyfU4Zg9zUVzealwty5DAnyRTGbaEChtKZ3RlMMRhO1qLUobu6dn1ItOu+sEX4M83OhytGFz/3GwYcfduk8hcdOYNRosTMa8A+y7oJwA8GM4X64aSHf3Y/9uw+BqXc7PXdEUoH6Jaa9dvitcnYm3lUdrd2T2fKM3OzSakyKBuf6OoaMGdbFC/WsLpXuBgYGctFFF+HbQ6sjjh07lrlz57ZZgCvEgDRuHEPL1Gr64+kFlqf3HVMLaSfmHoFzzsE+LBS/6nKg96YhV9efvN3k6imN3PqCoiiW0ZT3Ji/A9O9nu/SBmJuhNnILrNOh1Uh/m+5ystcye6x6K3aj32jY17m1b3pDcgfb4bdl4rgwNCYj2Q1a8lqYKdg0syeiPA8lqv2Otn3B+vOLhBjMXFwYqqhvFscb3yAMRhNJeWqxXqyHAv7+EBZmmYbcWw3dmmb2OBgacPSS2XN95bJJobg7akn3CWFLnZPatr6TcnKlkVtPuyBGvd2xYeR0zN9Y95ZPUaWefHtXFLOJsdFdX9LA7cxpjC1MByAh4/TRlIxjakuEqLoycLSNujRJUoSwsqE+6i3OpoZuRwqqqDUpuOlrGDYtRt0pNPTkNOReao3ftAKyq6yA3KdcHe24Kl4thFw9eQF8+mmnz5HbOOsrRBq59ZhZI/1xVMyc8A7i0JYEq8aSnKF+QRlamoNrzLiun2j6dOKy1S7xCal5p23OyFUTl0i7hq5fo4dJkiKElQ2NVIv0jter9SBNRbMxeUfRzpmj7hQWRlBTklLWO9+WK2UFZKu5YVokCrBlaBzHtv0Ohs4lG7k6tU4pxFX62/QUV0c7Zg31AmCjwRMKCto+oBclJx0HILokE4I72SPlVP7+xJvU95c9LSQp6eVqQ8AoD9tpQSBJihBWFhWjtkgv1zhSWl1P4kG18j628NjJxeACAwmoUd9c8gtaX4aiO06OpEiS0tfCfV04d7Q/AO8PPRNO6R/VEbmNtdTB3lLw3JMuiIsEYMPIaV26DddTkjIa2+Fra7u9plb8MPX37HA16Oqaj5hkNKhJbkSgV7eu0ZMkSRHCypwnTSBYpxbKHs8tJzFdHdqN9dKAa+PKylotQXZqYWtvNXSzdJvVy+0ea1h8plpr8Hn0uejWd64GIses1g8ED/Hq6bAGtXNGB2CPiaN+ERz7YZvV4kjWqcXU0f7dn/06ZHocEWW5mFD4/ZRZPvUGEzlaNcmNGhrU7ev0FElShLC28HCGVqhDyQcSj3K0Xh1qjZ3YfAXSQFf1+fxKfa+EUdX4rUpGUqxj+jBfRjqZqHFw5rOUkk4d29TILSRcGrn1JE9ne6YHqAng93n10ND3tRolVXpyUWMYN6IHkoczzzxZl3Ls5LI0WWU1mBQNrvoa/Ed3vTi3p0mSIoS1KQpD7dTx+nUHizErCiEVhQyZN7vZboE+6qhKfu/kKFRVqbOM3KRw1ioUReGm2SMBeC/0DIyHDnfouMrKWnSO6u9G0DDrr1o70FwwfRQAGyImw7a+H01JylGXxBhako1b9Njun3D4cOIr1AV696ScXKg344SasESU56EMH9796/QQSVKEsAFDvdUOr/vr1JbUscXHYfLkZvs0tcavNmuorOv5b3TVOnV2kVt9LbTQeFH0vkumDcPTUMcJ7yA2/W9zh47JS1ObAXrWVuIWajvD9APF3HGBaMwmkgOHk/Xtz31+/eTMxnqUgmMwrhsze5ooCnEh6shbYkkD9Qb1VlL6UfX3KLKmFGxoAV9JUoSwAUMjA5r9PNFdAW3zmRouYSF41Kn1KL3R0K2qsnEkBaNNLNE+GLk42HG1l/p3uzqr9bXQTpWTqc7SCK4rP+13RnSfr5sjU9zVpSi+P1zUzt49L+GQ2rtkQnk2BAb2yDmHTYnGu6YCPRqSc9WRmswcdT2fSE0vDdV2kbwTCWEDhsY0H16NjY44fafQUEtDt97olVJVrZ7TVdN7awOJ9l138RQ0JiPbvCI5mprV7v65ueqHS4jx9A6iomdcME2tD9vgNRzKy/vsuvUGE7sL1H+X05313Z7Z00Q5c8bJupR0daQmo0y9TqS7XY9co6dIkiKEDQiOj8GpQX2TsDMaGD9vxuk7hYVZGrr1RtfZqsY1gWzsPWrQCYsewdxCtR5l9Ve7292/qZFbsF3HRl5E553XOBV5b+hYqo+l99l1D2SXU2NS8KmpYFRUDxZFT5xIfMFRAPYkq4lwul5NgCIDbOdWD0iSIoRN0Li7E1mtJiCjK3JxGjPq9J1OaY1f0AsN3aqbZvfYy9uCtd0Uon5g/K9ES0VN2/VHOZVqchksjdx6TZCnMx4N6khVTmMNUF/Ykaa+J0w7cQDNpEk9d2IHB+K81X/nCTmV6A1GcjXq9ObIqJ65pdRT5N1ICBsxVKt+2MQ6NbQ8rDtkCAE15QDk5ZX2+PUrGxcYdHOQDztrm3rxLEYXplOrseeT39r+5p7b+A1YGrn1rhCj+sUg50Rhn11zxxH1WtMyD8C55/boucdPGI5TQx1lJi2bU4tOTj8eaRsLCzaRJEUIG3HDuWOYVJHFDVe2cKsHQKM52dCtWNfj169uUKv83ZxlNXJrU+LiWHzsVwDe+/UoRlPrdUI5itpDIyTAqy9CG7RC7dV/e9lFlX1yvboGI7+fKAdguqEYhvZs7xKHs2YQm3sEgM92ZwIQWZZrU9OPQZIUIWzG1MvO5X+v38HIsya3uk9vNnSraixpcHORJMXqNBouHuOLV62OnDr46VDL68YYTWby7dXp4sFh0sitN4W4qf/2snV9M/tlb2YZ9WYIrCwmakp0jxXNWkybRnyOWjy76Yh6GzmysggCAto6qs9JkiJEP2Jp6FbX8zNwqkzqm6Cra/dbb4vuc1o4n0WJ3wOwentGi/sU6moxarRoTUaGDJVGbr0p1E9NBnPqezhZaMWONDVxmJ55AKWHb/UA4OlJnL1aZ2NsfDuJVOp6PhnqJklShOhHggK9ASg121HX0LOzOapQa1Hc3SVJsQnnnMN1h35BazKy83gJh/NPv8WXm6X27QisLEYbJklKbwoN9QMgW+mbfx/bD6ujZ9My98M55/TKNSaNCkZjOvk+Eulme/VokqQI0Y94hgTi2KAONxf24LCzwWiiTlHnHrt6SrdZm+DsTMj0Scw7oq6I/O+NqZjNzUfQcjLURm4hNWXgLMllbwodGgJAjrMX6Hv3lo+uroEDeWrty3TH2h5r4vZH7mdOZXRRhuXnKH/b+7cvSYoQ/YgSHkZQY6+UnmzoVq0/+W3K1cu9x84rumnhQh7YthZ7k5GfDxfyzYG8ZptzG2d5BZukkVtvC4kKBqDIzYe6zPab7HXHnvRSTChEluYSMr31GrVuO/NM4hubugFERNpWPQpIkiJE/xIWRkBVU0O3nvtgqqo3AOBgaMDRWxYXtBkXXcSI0mz+suNjAJ5Yf5Cy6nrLZmnk1ne8XB1wbWy4mHsks1evdbI/yv4en3rcTHg48TX5ALjpa/AbEdl71+oiSVKE6E9ObehWWt1jp62qU5MUWQHZxgQEwNSp3LXzM0ba1VNSXc8/vzn5zTe3Sm30Jo3cep+iKIQY1KQwO7Pl2VY9ZcdhdcRs+okkmDWrV681K8KDydkpXP/7tzY3/RgkSRGif/HzI6C2HIC83JIeO22VXk1SXOtrJUmxNVdfjYPJwNOfPokC/G9fDptT1SZfOY2N3EK8Xa0Y4OARqlX/neQUlPfaNUqr6zlUrI7YTPXWgJdXr10LwH36FL5Y+zCP7FgL4eG9eq2ukCRFiP5EUQjSqkP7BT3Y0K0pSXHTy0iKzbnrLpg2jYlHf2dxjrqWz/99mUy13kAuaiO3YGnk1idCXNQRq+zynl87q8lvx9UvH6OKMvCfObXXrmNx3nlgbw9TpoCd7S3cJUmKEP1MoKv6RpLXk4WzlWrLbzcZSbE9dnawdi14ePDQJ08TqujJKa9l+dcHqbBzAiA4XBq59YVQH3XpgZxaU69dY/sx9XZub7TCb9HIkXDgAHz1Ve9fqwskSRGinwlsHNov6ME3yqpy9V67W30NuMvsHpsTFQWvv45Lg56Vn/wLgE8T1IXu3OuqcI+QHil9ISTIB4DsxhGs3rDzkFqPMiP3IMxoZYmMnjZ6NPj79821OkmSFCH6maAhXgAUmOzaXNOlM6p0apLiajKARt4WbNI118D113NW+u9ccXyn5ekQXRGESpLSF0KiggDIcfAAc893fc6rqOW4rgGNyciUEHfpfYMkKUL0O35hAWhNRoyKhuKq05tKHdtzkKn3fMCLf3uzw+esqlSnM7srhh6LU/SCV16BoUP529er8KtXE8uQqhLw8bFyYIND6Ei1sDTfzYf6/J6f4bOzcepxdH4anrP6aBTFxkmSIkQ/ow0PY0iV2sQrv+L0upQ3Pt1Bvqs360s7XgRXXa2ex1XeEWybhwesXYtXQy3PrHsWj7oq5pYetbn1VgYqP283HA31mBUN+Yczevz8O5rqUXq7P0o/Im9JQvQ3zRq6NU9SiitqWI96b/mEqy8GXceWla+qUUdkbHDpDvFHU6fC8uWcczyB/auu5ur6E9aOaNBQFIWQenVWXXZGXjt7d47ZbGbnYbWx2vSioxAf36Pn768kSRGivwkNtbTGLyhpnoR89Mmv1GvVJeUNWjty9iZ36JRVdWpTMDcHeUvoF/76V5g5EwUgMtLKwQwuIYra8Te7B/sUAZworSGnxoi9sYG4YUNscjqwNcg7khD9jY8PAbUVAOTlFluebjCaWHNUrVNQzOrMn/SDxzt0yqa1e1wdZCilX9Bq4dNP4ZFH4LHHrB3NoBKqzvomu6ymR8+7/Zia9EzMTcVl9swePXd/JkmKEP2NohDUuFZLQVGF5ekNv5+gUOuMf1Ups0qOAZDZwfbdlQ1qUuPmbN/DwYpeExAATz2lTh8VfSbUS51xk1Pdc+slmc1m1u1Tp5T3WX+UfkKSFCH6ocDGzpd5p3S+fHdjEgDXHd/OyGHqVMn0ko4tQtj0fuvm0nv9H4QYCEID1GaH2caeS+g/25vNrowyHBv0XJaXCOPH99i5+ztJUoTohwI91W9zBXXqCEhiVjn7qjXYGxu4JsafyAh1yfWMho7dvqkyqW8Fbq5OvRCtEANHSEQgADn2bj1yvsLKOp5sXDTygW0fEhEfLb2KTiH/J4Toh4Ia12rJM9phNpt5b/MRABYc2or/tX8ictxQADKdfaCsrN3zVTW+Fbi6u/ROwEIMEE29UvJcfTBUdmz2XFueWH8QXZ2B8YXHuXXPl3D++d0+50DSqSQlOTmZ+Ph4vL29Wbp0KeZ2Ou6ZzWbuvPNOfHx88PLy4qabbqK2trbdbUKItg0JVddqqVO0pBVV8c1BdVXcG8tTIDqayAh1e5ZnAIak9mf4VCnq0LW7Z898OxRioBoSMgR7owGjRktBN3ulbEzO57ukfLQmI09/+wJ2c86F667rmUAHiA4nKXq9ngULFjB58mQSEhJISUlh9erVbR6zZs0aUlNT2bdvH1u3buXgwYOsXLmy3W1CiLY5RYThU6MWzT73wxEaUJiUc4gJ82cBEOjhhKPJgEFrR/b+1DbPZTCaqGuctuzqJev2CNEWjUYhuK4cgOzjOV0+T0VtA8u+UuvIbt/1BeO8HeCTT2Tq8R90OEnZsGEDFRUVPP/88wwbNowVK1bwzjvvtHnM7t27ueKKK4iIiCA6OppLLrmEY8eOtbtNCNGO0FACGnulbEhWG0AtTlgPixYB6htppKIW1aYfy27zVE3TjwFcvT16I1ohBpQQs/pvKyenuNV9qvUGskpbn6a88rtDFFbVM7Qkm3tTNsLXX4O3d4/H2t91OEnZv38/U6dOxcVFvWcdExNDSkpKm8eMGzeODz74gIKCAjIzM/n444+ZO3duu9taotfr0el0zR5CDFphYQRVnnyDDKgs4Xx/BSIiLM9FuKujI5kFbf9b0TU2cnMwNODo49XzsQoxwIQ4qKUO2cWt16Tc8somznpmExf9+yfe35lBRU2DZduOtGI+3pMFwFM/vIrTJx/BiBG9G3Q/1eEkRafTERUVZflZURS0Wi1lbRTl3XrrrVRVVREYGEhkZCRRUVHceOON7W5rycqVK/H09LQ8wsLCOhq6EAOPpycB+pPJx/X7vsV+0dXNdokKURedy6g2trli69FC9Y02vDwPPD17IVghBpZQDwcAcipbXpAzr7yG34rUzrQHS/QsW3eQKf/YyH3v7uDXI0U8+v5vAFz3+7dMefQuOOecvgm8H+pwkmJnZ4ejY/MeCk5OTtTUtD6ctWrVKry8vMjMzOTEiRMYDAaWLl3a7raWPProo1RUVFgeWVlZHQ1diIFHUQjSqrdpHAz1LEr+Ca68stkukSNCAUh39oHCwlZPlZSpLlYYnX9MkhQhOiDEX70tmtPQ8kfo5h9/B2B8/jGW/fwWowvT0aNhXWoZN/x3N5l6hSBdEY+McoA77uizuPujDicpPj4+FBUVNXuusrISBweHVo9Zu3YtS5cuJTw8nLCwMFauXGmpY2lrW0scHR3x8PBo9hBiMIvXqCMg1+37Dt9Z08HPr9n2yCD1/namVzAktz7DJ+mEmqSMLzgG7lI4K0R7QkPVRTyztS1P2d+cmAnAedpybv7mP2yYCOv2vcs1+zbgrq9GazKyonAH7i8822cx91cdTlLi4+PZuXOn5ef09HT0ej0+Pj6tHmMymSg85Rtcfn4+RqOx3W1CiPZN89aw+5Xr+Nsv78A115y2PdJPfQPN8gqgIflgq+c5mKPOEoquLVbXhBFCtClkhFpukOvsjamh+S2feoOJ7Xq12eLZsREQEIByz91M+P5zVvznIXYPSWeHfiuz335GZvJ0QIf/D82cOROdTse7777L4sWLWbFiBXPmzEGr1VJeXo67uzvaP7zBnXXWWTz11FNotVrq6+t5+umnWbhwYbvbhBAdEBbGkOpycHWFFv7tBLg74YSROo2W7EPpRJ1+Boqr9OTVmVHMJsbGDO31kIUYCIKGh6E1JVNvZ09RWhYBo0/+60o4kE6VnSN+1WWMv+QPa/BERuL86CM493G8/VmHkxQ7OzvefvttFi1axNKlS9FoNGzevBkAb29v9u3bR2xsbLNjnnzySXQ6HQ8//DCVlZXMmzePVatWtbtNCNEB06ap/73hBjVR+QONRiHSCQ7XQUZWcYtJSlLjKEpUaQ5u8y/oxWCFGDjsHOwJrK0gx9WH7KMnmiUpW35JBJyYWZaOJkwas3VXp8aaFi5cSFpaGnv37mXq1Kn4+voCtNp51svLi/fff7/T24QQHTBvHqSkwPDhre4S6e/O4awaMir06gwfRWm2PTk5A4DoguNwwaO9Ga0QA0qIsZocfMjOKmTyKc9vyqsDeydmh8oSEz2h02v3BAYGctFFF1kSFCGEFY0ZA/atr8Ya0dgeP8PZB7JPb+qWlJQBQLSLSRpJCdEJoXZqDWVO4clWADklVRyx90JjMnLWefHWCm1AkQUGhRjAooaos3XSvVue4ZOsU1dRHj9B6lGE6IxQV/VGRLZOb3lu8w97AJhUcAyvmdOtEtdAI0mKEANYpJ9aq5LRQpJSkldMroOaxIybP7vPYxOiPwvxUxfjzDmZo7D5gDpaebZTjczc6SGSpAgxgEU1JinZnkOoT26+jEXyd78CMLSyEPfoMX0emxD9WWiw2n4jW1Hn6ugNRrY3qP/ezp4sI5M9RZIUIQawIe6OOGvMmDTa01ZsTd57BIDxriZrhCZEvxYyTO3onOPoidlkImHvMWrsHPGvKmXcxee2c7ToKElShBjAFEUhwktdziKjpAaaGiYajSSVqOPU48eEWys8IfqtoFGRKGYTdfaOlOQVs2nTfgDOrsxECQiwcnQDhyQpQgxwUY3t8TNcfCE9XX3yt99I8lG7Zo6fHmOt0ITotxw93RlSo/YZyknNZHOBuqDg2RGyZEtPkiRFiAEu0l8t8Du1eLbs643keKrf9sZHtL60hRCidSEN6vpZv6XkcMzRG63JyJnnT7VyVAOLJClCDHCRvmpTqXSfk0lK0m/qfyMdjHg4td5nRQjRulCNOnry4fFaACYXHsNzapw1QxpwJEkRYoCL9D1lGvLBg5CeTnKdOj1y/NAh1gxNiH4txEVdry7TQb3FM8u1HjTysdqT5P+mEANc0zTkHA9/6g8egq+/JjlQbaUfHeVvzdCE6NdCvZsvFTh7SutLVIiukSRFiAHO390RFzsNJo2WrPwy+N//SGpKUkI8rRydEP1XSNDJeq6AyhLGLJSpxz1NkhQhBjhFUYjwb7zl4xFA+a69ZHkFAjAuWJIUIboqNCLQ8ueza3JQZP2rHidJihCDQJSlPX4QyQHDAAj3ccHTRYpmheiqkNGRlj+fPczLanEMZLK4gBCDwKnFs/VaNTGRWz1CdI9zcCCzTySS6erLWVefae1wBiRJUoQYBE5daLDURU1OxkuSIkT3KArv3n4m5uISlNhoa0czIEmSIsQg0DSSku4djFZRABlJEaJHzJqFYu0YBjBJUoQYBCL91IZuOV4BmBvfUseHSPtuIYRtk8JZIQYBfzdHXB20lgQlzMcZLxcHK0clhBBtkyRFiEFAURRLXQrAeJl6LIToByRJEWKQaKpLASmaFUL0D5KkCDFINNWlgBTNCiH6B0lShBgkTh1JkSRFCNEfSJIixCAxKtAdgEhfF7xdpWhWCGH7ZAqyEINEdIgnz1wRw+jGZEUIIWydJClCDBKKovCnuDBrhyGEEB0mt3uEEEIIYZMkSRFCCCGETZIkRQghhBA2SZIUIYQQQtgkSVKEEEIIYZMkSRFCCCGETZIkRQghhBA2SZIUIYQQQtgkSVKEEEIIYZMkSRFCCCGETepUkpKcnEx8fDze3t4sXboUs9nc5v5ms5k777wTHx8fvLy8uOmmm6itrW22j8lkYvr06Tz33HOdj14IIYQQA1aHkxS9Xs+CBQuYPHkyCQkJpKSksHr16jaPWbNmDampqezbt4+tW7dy8OBBVq5c2WyfN954g4qKCu69994uvQAhhBBCDEwdTlI2bNhARUUFzz//PMOGDWPFihW88847bR6ze/durrjiCiIiIoiOjuaSSy7h2LFjlu25ubk89thjvPzyy9jb23f9VQghhBBiwOnwKsj79+9n6tSpuLi4ABATE0NKSkqbx4wbN441a9Zw+eWXU1dXx8cff8yDDz5o2X7//fcTERFBVlYWO3bsYPr06a2eS6/Xo9frLT9XVFQAoNPpOvoShBBCCGFlTZ/b7ZWMNO3UIQ8++KD5rrvuavacn5+fubS0tNVj6uvrzdHR0WbADJgXLFhgNhqNZrPZbN6xY4cZMF944YXmxx9/3Dx8+HDzX/7yl1bP9fe//91yHnnIQx7ykIc85NG/H1lZWe3mHorZ3JFUBh555BEaGhp4/vnnLc+FhYXx22+/ERIS0uIxzz77LOvXr+eDDz5AURRuv/12xowZw3PPPcfNN99MSkoKO3fuRFEUsrKyiIiI4NChQ4waNeq0c/1xJMVkMlFaWoqvry+KonTkJXSYTqcjLCyMrKwsPDw8evTctkpes7zmgUpes7zmgaq/vmaz2UxlZSXBwcFoNG1XnXT4do+Pjw/JycnNnqusrMTBwaHVY9auXcs//vEPwsPDAVi5ciWzZs3iueeeIzs7mwsvvNCSYISFheHv709aWlqLSYqjoyOOjo7NnvPy8upo+F3i4eHRr/7ie4K85sFBXvPgIK95cOiPr9nT07ND+3W4cDY+Pp6dO3dafk5PT0ev1+Pj49PqMSaTicLCQsvP+fn5GI1GAEJDQ5tNR66qqqK0tLTVURkhhBBCDC4dHkmZOXMmOp2Od999l8WLF7NixQrmzJmDVqulvLwcd3d3tFpts2POOussnnrqKbRaLfX19Tz99NMsXLgQgEWLFrFo0SLmzJnD8OHDefzxxxk9ejQxMTE9+wqFEEII0S91OEmxs7Pj7bffZtGiRSxduhSNRsPmzZsB8Pb2Zt++fcTGxjY75sknn0Sn0/Hwww9TWVnJvHnzWLVqFQBz587l6aef5s477yQrK4vY2Fg+//zzHq8v6QpHR0f+/ve/n3Z7aSCT1zw4yGseHOQ1Dw6D4TV3uHC2SX5+Pnv37mXq1Kn4+vr2VlxCCCGEGOQ6naQIIYQQQvQFWWBQCCGEEDZJkhQhhBBC2CRJUoQYwMrLy9m1axdlZWXWDkUIITpNkpQ/SE5OJj4+Hm9vb5YuXdqxtQX6oeLiYqKiosjIyLA8N5Bf+7p16xg6dCh2dnbExsZy6NAhYGC/5s8++4zIyEhuvfVWQkND+eyzz4CB/ZpPdf7551tWat+yZQtjxozBz8+vWdfsgeDee+9FURTLY/jw4cDg+Ht+5JFHWLBggeXngfqaV69e3ezvuOmxevXqAf27DZKkNKPX61mwYAGTJ08mISGBlJQUy5vcQFJcXMz8+fObJSgD+bWnpaWxePFinnrqKXJychg5ciS33nrrgH7NFRUV3HXXXfz6668kJSXx6quvsnTp0gH9mk+1du1avv/+ewCKiopYuHAhixYtYufOnaxdu5ZNmzZZOcKek5CQwLfffktZWRllZWXs27dvUPw9HzhwgNdee83S1mIgv+ZrrrnG8vdbVlZGVlYWfn5+jBkzZkD/bgN0eIHBweDLL780e3t7m6urq81ms9mcmJhonjFjhpWj6nnnnnuuedWqVWbAnJ6ebjabB/Zr//rrr83/+c9/LD//8ssvZmdn5wH9mk+cOGH+4IMPLD/v37/f7ObmNqBfc5OSkhJzQECAedSoUeZ3333X/MILL5hHjx5tNplMZrPZbP7qq6/M1157rZWj7BkNDQ1mDw8Pc2VlZbPnB/rfs9FoNJ9xxhnmxx9/3PLcQH/Np/rXv/5l/vOf/zygf7ebyEjKKfbv38/UqVNxcXEBICYmhpSUFCtH1fPeeust7r333mbPDeTXPn/+fG677TbLz6mpqYwYMWJAv+awsDCuvfZaABoaGnjhhRe49NJLB/RrbrJkyRIuvfRSpk6dCqi/27Nnz7Y0ipwyZQp79+61Zog9JikpCZPJRGxsLM7Ozpx//vmcOHFiwP89v/HGGyQlJREZGcn69eupr68f8K+5SV1dHatWreKxxx4b0L/bTSRJOYVOpyMqKsrys6IoaLXaAVd0eOprbDJYXnt9fT3PPfccd9xxx6B4zfv37ycwMJCNGzfy0ksvDfjXvGnTJn7++WeeeeYZy3N/fM0eHh7k5uZaI7wel5KSwqhRo1izZg0HDhzAzs6O2267bUD/PVdVVfH3v/+doUOHkpmZyQsvvMCZZ545oF/zqT788EPOOOMMIiMjB/TvdhNJUk5hZ2d3WnthJycnampqrBRR3xksr/3vf/87rq6u3HrrrYPiNcfExPDDDz8wYsSIAf+a6+rquP3223n99ddxd3e3PP/H1zxQXi/AtddeS0JCAtOmTWPEiBG89tpr/Pjjj5hMpgH79/y///2P6upqNm3axPLly/nxxx+prKzkv//974B9zad64403uOOOO4CB/bvdRJKUU/j4+FBUVNTsucrKShwcHKwUUd8ZDK/9l19+4dVXX+XDDz/E3t5+ULxmRVH4//buHyS5KAwD+FugWJSZZgS12BB0oZYQ7uIYCLnV0pINtUS0Ce2NLTWENFRIcAeHyLb+LEHgFE1Rg3BIJaQagsLIoOcbPrxkNVb3cnx+cIZzpvfhPeg73MsdGxuTTCYje3t7WmdeWVmRaDQqExMTDeefM+uS9zu9vb3y/v4ufX192va5XC6LaZrS09MjIv//qEdHR+Xx8VHbzHWFQkEKhYKMj4+LSHPcbQ4pH0SjUcnn8/ZeKSWvr68SDAYdrOpv6J5dKSXT09OysbEhhmGIiN6ZT09PJZVK2Xuv1ystLS0yPDysbWbLsiSXy0kgEJBAICCWZcnCwoJkMpmGzBcXF9Lf3+9gpT8nlUqJZVn2Pp/PS2trq4yMjGjb54GBAXl5eWk4u7m5kbW1NW0z12WzWUkkEuLxeETk62+YTnfb5vSTu27y9vaGcDiM7e1tAMDc3BwSiYTDVf0e+fB2j87Zq9UqDMPA/Pw8np6e7FWr1bTNfHt7C7/fj83NTRSLRczMzCAej2vd51KpBKWUvSYnJ7G6uor7+3v4fD4cHx+jVqshHo9jcXHR6XJ/xO7uLiKRCE5OTnB4eIihoSHMzs5q3eeHhwf4/X6k02mUSiWsr6/D5/OhWCxqm7kuFotha2vL3ut8t+s4pHySy+XQ3t6OUCiEcDiMy8tLp0v6NR+HFEDf7Pv7+xCRL0sppW1mADg6OoJhGOjs7MTU1BTu7u4A6Nvnz5LJJHZ2dgAA6XQaHo8H3d3diEQiqFQqzhb3g5aXl9HV1YVgMIilpSU8Pz8D0LvPZ2dnME0TbW1tGBwcxMHBAQC9M1erVXi9XlxdXTWc63y3AYBfQf5GpVKR8/NzMU1TQqGQ0+X8qWbMzszNkVkpJdfX1xKLxaSjo8Ppcv5EM/a5GTPrfLc5pBAREZEr8cFZIiIiciUOKURERORKHFKIiIjIlTikEBERkStxSCEiIiJX4pBCRERErsQhhYiIiFyJQwoRERG5EocUIiIicqV/Pm1miF7Oi3oAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson相关系数: 0.9990809616575648\n",
      "p值: 1.2917144844778001e-101\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "code_fund = '588460'\n",
    "# plt.plot(range(len(clean_data_)),data['单位净值'][:31],c = 'b')\n",
    "db = ef.stock.get_quote_history(code_fund,beg='20230714')\n",
    "data = ef.fund.get_quote_history(code_fund,pz=75)\n",
    "# db_1102 = ef.stock.get_quote_history(code_fund,beg='20231102',end='20231102',klt=5)\n",
    "print(len(db['收盘']))\n",
    "print(len(data['单位净值']))\n",
    "plt.plot(range(len(data['单位净值'])),data['单位净值'][::-1],c = 'r',label = '单位净值')\n",
    "plt.plot(range(len(data['单位净值'])),db['收盘'],label = '收盘价（日）')\n",
    "plt.legend()\n",
    "# plt.plot(range(len(clean_data_[0])),clean_data_[0])\n",
    "# plt.plot(range(len(clean_data_[0])),db_1102['收盘'])\n",
    "plt.show()\n",
    "import numpy as np\n",
    "from scipy.stats import pearsonr\n",
    "# db = ef.stock.get_quote_history(code_fund,beg='20230801')\n",
    "# 执行皮尔逊相关性分析\n",
    "correlation, p_value = pearsonr(data['单位净值'][::-1], db['收盘'])\n",
    "\n",
    "# 打印相关系数和p值\n",
    "print(f\"Pearson相关系数: {correlation}\")\n",
    "print(f\"p值: {p_value}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'clean_data_' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\Lenovo\\jupyter_notebook\\Untitled.ipynb Cell 9\u001b[0m line \u001b[0;36m2\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Lenovo/jupyter_notebook/Untitled.ipynb#X26sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m db \u001b[39m=\u001b[39m ef\u001b[39m.\u001b[39mstock\u001b[39m.\u001b[39mget_quote_history(code_fund,beg\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39m20231031\u001b[39m\u001b[39m'\u001b[39m,end\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39m20231031\u001b[39m\u001b[39m'\u001b[39m,klt\u001b[39m=\u001b[39m\u001b[39m5\u001b[39m)\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/Lenovo/jupyter_notebook/Untitled.ipynb#X26sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m plt\u001b[39m.\u001b[39mplot(\u001b[39mrange\u001b[39m(\u001b[39mlen\u001b[39m(clean_data_\u001b[39m.\u001b[39miloc[:,\u001b[39m3\u001b[39m])),clean_data_\u001b[39m.\u001b[39miloc[:,\u001b[39m3\u001b[39m],c \u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39mr\u001b[39m\u001b[39m'\u001b[39m,label \u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39m估值iopv\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Lenovo/jupyter_notebook/Untitled.ipynb#X26sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m plt\u001b[39m.\u001b[39mplot(\u001b[39mrange\u001b[39m(\u001b[39mlen\u001b[39m(db[\u001b[39m'\u001b[39m\u001b[39m收盘\u001b[39m\u001b[39m'\u001b[39m])),db[\u001b[39m'\u001b[39m\u001b[39m收盘\u001b[39m\u001b[39m'\u001b[39m],label \u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39m收盘\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Lenovo/jupyter_notebook/Untitled.ipynb#X26sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m plt\u001b[39m.\u001b[39mlegend()\n",
      "\u001b[1;31mNameError\u001b[0m: name 'clean_data_' is not defined"
     ]
    }
   ],
   "source": [
    "db = ef.stock.get_quote_history(code_fund,beg='20231031',end='20231031',klt=5)\n",
    "plt.plot(range(len(clean_data_.iloc[:,3])),clean_data_.iloc[:,3],c = 'r',label = '估值iopv')\n",
    "plt.plot(range(len(db['收盘'])),db['收盘'],label = '收盘')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "#用公式计算发现具有相似的趋势性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'clean_data_' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\Lenovo\\jupyter_notebook\\Untitled.ipynb Cell 10\u001b[0m line \u001b[0;36m5\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Lenovo/jupyter_notebook/Untitled.ipynb#X12sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mscipy\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mstats\u001b[39;00m \u001b[39mimport\u001b[39;00m pearsonr\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Lenovo/jupyter_notebook/Untitled.ipynb#X12sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m \u001b[39m# 执行皮尔逊相关性分析\u001b[39;00m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/Lenovo/jupyter_notebook/Untitled.ipynb#X12sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m correlation, p_value \u001b[39m=\u001b[39m pearsonr(clean_data_\u001b[39m.\u001b[39miloc[:,\u001b[39m3\u001b[39m], db[\u001b[39m'\u001b[39m\u001b[39m收盘\u001b[39m\u001b[39m'\u001b[39m])\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Lenovo/jupyter_notebook/Untitled.ipynb#X12sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m \u001b[39m# 打印相关系数和p值\u001b[39;00m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Lenovo/jupyter_notebook/Untitled.ipynb#X12sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mPearson相关系数: \u001b[39m\u001b[39m{\u001b[39;00mcorrelation\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'clean_data_' is not defined"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import pearsonr\n",
    "\n",
    "# 执行皮尔逊相关性分析\n",
    "correlation, p_value = pearsonr(clean_data_.iloc[:,3], db['收盘'])\n",
    "\n",
    "# 打印相关系数和p值\n",
    "print(f\"Pearson相关系数: {correlation}\")\n",
    "print(f\"p值: {p_value}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "估计的斜率 (a):0.9945595270439016\n",
      "估计的截距 (b):0.004743715936154302\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avg_loss: 2.0767066732929607e-10\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "import numpy as np\n",
    "from scipy.optimize import curve_fit\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "db = ef.stock.get_quote_history(code_fund,beg='20230714')\n",
    "x = np.array(db['收盘'])\n",
    "y = np.array(data['单位净值'][::-1])\n",
    "\n",
    "# 定义线性回归模型函数\n",
    "def linear_model(x, a, b):\n",
    "    return a * x + b\n",
    "\n",
    "\n",
    "# 使用最小二乘法估计参数\n",
    "params, covariance = curve_fit(linear_model, x, y)\n",
    "\n",
    "# 提取估计的模型参数\n",
    "a_est, b_est = params\n",
    "\n",
    "# 打印估计的参数\n",
    "print(f\"估计的斜率 (a):{a_est}\")\n",
    "print(f\"估计的截距 (b):{b_est}\")\n",
    "\n",
    "# 绘制原始数据和拟合线\n",
    "plt.scatter(x, y, label=\"原始数据\")\n",
    "plt.plot(x, linear_model(x, a_est, b_est), color='red', label=\"拟合线\")\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "print('avg_loss:',np.average(linear_model(x, a_est, b_est) - y))\n",
    "get_like = linear_model(data_fund['收盘价'], a_est, b_est)\n",
    "plt.plot(range(len(get_like)), get_like,c = 'r',label = 'IOPV')\n",
    "plt.plot(range(len(get_like)),data_fund['收盘价'],c = 'b',label = '收盘价')\n",
    "plt.legend()\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAikAAAGbCAYAAAABeQD9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAACFfElEQVR4nO3dd3xUZfb48c+U9F4hPaEXCSACARF7WRDXrui66i7Wdd3yXXZ1i/vbhrquuq5rW3XFtXexYRcQpXcIBAiBhPQ+qZPMzP398czczCQZSCTJTJLzfr3ympt7597cS6I5eZ5zzmPQNE1DCCGEEMLPGH19A0IIIYQQ3ZEgRQghhBB+SYIUIYQQQvglCVKEEEII4ZckSBFCCCGEX5IgRQghhBB+SYIUIYQQQvglCVKEEEII4ZfMvr6B78rhcFBSUkJERAQGg8HXtyOEEEKIHtA0jYaGBpKTkzEajz1WMmiDlJKSEtLS0nx9G0IIIYT4DoqKikhNTT3mewZtkBIREQGoh4yMjPTx3QghhBCiJywWC2lpafrv8WMZtEGKa4onMjJSghQhhBBikOlJqoYkzgohhBDCL0mQIoQQQgi/JEGKEEIIIfySBClCCCGE8EsSpAghhBDCL0mQIoQQQgi/JEGKEEIIIfySBClCCCGE8EsSpAghhBDCL0mQIoQQQgi/JEGKEEIIIfySBClCCCGE8EsSpAghhBDCU1szrHkAPv9/Pr2NQbsKshBCCCH6mMMOO16BL/8GDSVgNMPJP4TYUT65HQlShBBCCAEHv4DP7oHy3erzqHQ4+x6IzvTZLUmQIoQQQgxnZbvhsz9A/pfq86AomP8rmHUzBAT79NYkSBFCCDEo1Le088bmIi49OZXYsEBf387QUHMInjkHbC1gDIBZN8H8pRAa6+s7AyRIEUIIMUg8sSqfJ1fnsyqvkhd+PAuDweDrWxr8tr2kApSkaXDFcx65J81tNkIDfRsmSHWPEEKIQeGbg1UArD1Yxae55T6+myHA4YCdr6vtU+/0CFDa7Q7OfnA1P16+iQpLq49uUIIUIYQQ/ubQalh+IZTu1HdZWtvZU1Kvf/7XD3Npbbf74u6GjsJ1UF8IQZEwfoHHoU/2lFFa38qOo/VEh/puak2CFCGEEP5l9d/h8Nfw7m1gtwGw+XANDg1SokMYGRlMUU0Lz3x9yMc3OsjtfFW9TroIAkI8Dv3v2yMAXDM7nUCz70IFCVKEEEL4j5Za9Rc+qFLYjf8BYMOhGgDmjYnn7gUTAHjsq3xK6lp8cpuDXnsr7FmhtrOv9jiUW2Jh4+EazEYD185O98HNdZAgRQghhP848DlodjA5pxi+WgYNZaw/VA1AzuhYLpqazMzMGFra7dy7cp8Pb3YQ278SrPUQlQYZp3ocemH9YQDOP2kkIyJ9W4IsQYoQQgj/kfeRes25HVJmQFsD7St/y65ilY8yOysOg8HA/7toMkYDvL+jhA3OAEb0wo7X1OuUK8DYEQrUN7fzzrZiAK6fk+mDG/MkQYoQQgj/YGtTXU8BJiyEhQ8CBgJy32K2YQ/psaEkR6vcicnJUVw9S01F/L/3c7E7NB/d9CDUVA0HP1Pb2Vd5HHpjSxGt7Q4mjIxgZmaMD27OkwQpQggh/EPht2oKIixBjaIkT4eZPwbgz+blzM2I8Hj7r84bT2Swmb2lFl7ZWOiLOx6c9rwNDhskTYXECfpuh0Pjf+tUwuz1czP9og+NBClCCCH8Q97H6nXs+WA0qe2zfk+dIYqxxmKu5UOPt8eGBfJ/540H4B+f5lHX3DaQdzt47XBW9XRKmF29v5LCmmYig818f1qyD26sKwlShBBC+J6mdeSjjL9A391ojGBZ+2IAJh98EuqPepx27ex0xo+IoK65ncdX5Q/Y7Q5aVQeheDMYTDDlco9Dz687DMCVp6T5vNOsiwQpQgghfK9yH9QdAVMQjDpT3735cA1v2Oax0zgBY3szfPJbj9PMJiO/PG8cAB/sKEHTJDflmHY6E2ZHnwXhifruw1VNrMqrxGCAH+Rk+OjmupIgRQghhO/lrVSvWfMhKFzfvaGgBg0jn2X9Rv31n7uiY7Vep9PHJRASYKKkvpU9JZaBvOvBRdM6gpSpnlM9L6xXuShnjEsgMz5soO/MKwlShBBC+J4rSBn/PY/drv4o6RNn6km0bHrW4z3BASbmj4sH4DNZ08e7wvVqtCowwqMNfnObjdc3FwHww7mZPrq57kmQIoQQwrcaK+HoJrU9riMfpclqY9dR1R8lZ1QcTLtWHcj/UnVMdXPOxBGABCnH5N4GPzBU3/3uthIaWm1kxIVy+tgEH91c9yRIEUII4VsHPgU0VRIblaLv3nKkFptDIyU6hLTYUHU8Ihnam9XaPm7OnjgCowFySy0crW0e4AcYBNpbYM87ajv7Sn23pmn8z5kwe11OBkaj78uO3UmQIoQQwrdcVT3jup/qmT0qVu0wGDoqf1znOMWGBXJKhnrfF3sr+u9eBxtNg9z34IlTobVeBXmZpzkPady7ch/7yhoICTBxxYw0H99sVxKkCCGE8J32Vsj/Sm27lR6DSpoF51SPiyuQ2f+J+gXs5pxJqlrlu0752OwO7v1or55EOugVbYT/XgCvXwc1+apJ3kWPgtGE3aFx99u7+M8atZL0bxdMICo0wMc33JV/FEILIYQYng6vhfYmiEiCpGn67uY2GzuK6gDIyXILUrLmQ0AoWIqhbKeaAnI6d9JIln20j/WHqqlvaScq5Bi/dDVNjcy4+d+6Izy15hAGg1ptOcuPqlx6pTofvviTqoQCMIfA3Dvg1J9BUARtNge/eH07H+4sxWiA+y7N5sqZ/jeKAjKSIoQQwpf0qZ4LPIKGrUfqsDk0kqOCSYsN6Xh/QLDq8QEdFUFOWfFhjEkMx+bQWL2/0vvXLN4K/8yGd27TR2MqLK08/Nl+QO1yjTAMOsVb4PEcZ4BigOk/gDu3wlm/h6AIWtrs3PzCZj7cWUqAycC/rznZbwMUkCBFCCFEH+pVMzVNg/3OVvhuJbHQkY+SMyqu6xoyrgqgTkEK9KDKp+ogvHQ51BfCjpf10YZlH+2lwWojOSoYgLe2HqWiobX7a/iz9U+AvQ1SZ8Kta+H7j0GkanFvaW3n+v9uZFVeJcEBRp65fiYLpiT5+IaPTYIUIYQQfWLpGzuY/8BXHKxo7NkJZTvVtE1AqJrGcdMladbduPMBA5RuB0uJx6FzJ6kgZVVeBW02h+d5DWXw4iXQXA2BzoZxH9/Nhn2FvLu9BIMBnrxuBienR9Nmc/DcN4d79hz+wtoAez9Q2xfcDyNP0g81t9m45un1bDxcQ0SwmRd+PJvTx/lXuXF3JEgRQgjRO1v/B6vuB4dd36VpGu/tKKGopoUbl2+kqtF67Gs47LDtRbU96kw1jePU0mZnx9E6oFPSrEt4IqSeorZdIzFO09OiiQ8PoqHVxkZn4i2gKltevAzqCiF2FNy+DqLToaGEw2/fA8A1s9LJTo3m1tNHA/Di+iM0tLb34B/ET+x9H2wtEDcGUk72OPTuthJ2F1uIDQvklZtymJnZTfDnhyRIEUII0XM2K3zwC1i1DD78pZ7TUdlgxeocuSiqaeGm/22mtd3e/TXyv4SnToeN/1GfT77Y4/DWwlra7RpJUcGkx4Z2PR/cpnw8gxSj0cA5E11VPmVqZ3srvHINlO+G8BHwg7dVgPK9vwNwqfU9ZoSWs/R8taLyORNHMCYxnIZWGy9vKOzpv4zvua9u3GmK7JM96t9iyWlZnJQSNdB39p1JkCKEEKLnag6Bw6a2tyyHVfcBUFijGqjFhAYQFRLAtsI6fvHadhwOtxyVst3wwqXwwiVQvguCouC8v8KUK/S3NLS2s/zbwwDMzortmo/i4mqff2gVtDV5HHLlpXy+twLNboO3l8CRtRAUCde+CbFZAJSOPIMvtRkEGOw8Ef0y0c5qIKPRwM3zRwHw7NoCrDYvwZY/qS+GgjVq261ZG6h/02/zqwA4b9LIgb6zEyJBihBCiJ6rOqBeXTkdq++DTc/oQcrEpEieum4GASYDK3eXcf/H+1TeyLs/gSfnQf4XYAyAnNvhZ9th7k/1v/pX76/k/IfX6EmvF09P6fzVOyROUqMhdqsKVNzMGxtPSICJ4rpmat+8U02DmALh6pchKVt/318/2Ms9bddhJZDEmk2w60392MXTUhgZGUxFg5V3txWf2L/ZQNj1BqBB+lyI8VzFeFVeJe12jVEJqvppMOlVkLJ7925mzpxJTEwMS5cu7XEWt8PhYO7cuTz44IMe+//0pz8RGxtLUFAQl1xyCQ0NDb25HSGEEAOt+qB6nbAQTr9LbX/4KwLz3gcgPTaUnFFx/P3ybMJoIfzb+7D9czpsfxHQYNLFcMdGuOBeCFV5EfUt7fz6zR1c/9+NlNS3kh4byis35XDG+ETv92EwdDR261TlExxg4rSx8fzM9Daxe18CDHDZM5B1mv6eNfsr+XBXKSUkUnfKnWrnp79TuStAoNnIj+epEZen1hzyHBHyNx6rG1/V5fCnzqBvsI2iQC+CFKvVyqJFi5gxYwabN28mNzeX5cuX9+jcJ598kvr6eu68805930svvcRLL73Exx9/zJ49e9i7dy/33Xdfrx9ACCHEAHIFKXFj4Iy7YMaNgMYF+/9AjjGX9LhQsLdzie1jNkUs5afmdzE7WqmPnwE//hyufF4lrjp9ua+c8x9ew+ubj2IwwI2nZvLxz09jzuhuEmY7G+/WfdbhWclzS9gqfhHwlvpk4YMw6fuASvDdWljLPSt2A/DDOZmMuODX6p4ay/XpK4DFs9OJDDZzqLJJ/0Xvl8p2QUWuGi1yPqeL1Wbnq31qmYDzJo/wxd2dkB53nF25ciX19fU89NBDhIaGsmzZMn7yk59w4403HvO8kpISfvvb3/L2228TENDR/a+oqIjnn3+eWbNmAXDVVVexadOm7/gYQgghBoRruidujBrNWPggNFdh3vs+/wl4kCO1wOOvQfUBQoHKwFR+33gFnxafQsCTVYDnqEebXQUXWfFh/P3y7N5VnWScqvJMmiqgZGtHxU/uCk7e9VcA/mm7lCvHXkNgo5V3thbz+uYiDjhLpOPDg/jleePAHAALHlDVPxueUqstjzyJ8CAzP5yTyb+/OsiTq/M5f/II7zkyvuQaRRl3AYTEeBxaf6iGRquNhIggpqVGD/y9naAej6Ts2LGDnJwcQkNVpnV2dja5ubnHPe/nP/85GRkZFBUV8e233+r777rrLubMmaN/npeXx9ixY71ex2q1YrFYPD6EEEIMMNdISrzz/9dGE1z6DFsMk4k0tDBlx1+h+gCExsGCfxD1f1uxjVuIphloszu6fBgNcNNpWXx052m9L4s1B7p1n3V2ri34Gt5aggGNT0MW8E/bZdz43CZyln3B3z7ay4GKRoIDjFw6PYVXb84hMtj5x/OYc2DiRaDZ4eO79C9xw6mZBJmNbC+q09cS8isOe0cuzdSruxz+1FnVc+6kEX63wnFP9HgkxWKxkJWVpX9uMBgwmUzU1tYSExPT7Tnr1q3jjTfeYMGCBeTn5/PXv/6V888/n3//+98e79u/fz/vvPMOW7du9fr17733Xv70pz/19HaFEEL0teYaaHH+oo4dre9u0QK4oeUXvBi4jOzAEgxzfgKn/hyCIwkEnr1hJuWWVuzd5HWEBZmPvcbO8YxfALnvqlLkyZfAq9eojqsTF3F4xD3w8QHyylW+Y3ZqFFeeksZF05I7ghN35y+Dve/B4a+hsQLCE4kPD+KKU1J5cX0hT67O775viy8dWgWNZRASC2PO9TjkcGh6EvJ5kwbfVA/0Ikgxm80EBQV57AsODqa5udlrkPL0008ze/ZsPvjgAwwGAzfddBMZGRn89Kc/Zfx4VY/ucDj40Y9+xJIlS5g8ebLXr3/33Xfzy1/+Uv/cYrGQlua/6w0IIcSQ45rqiUyFwI7+JUdrm2kglOuNf2Pbb86EgJAup46IDO6yr0+MPRcMRqjYA//7PlgtkDEPLn2Gq2wmdpQ0khgRxJWnpDExKfLY14pOgxFTVHn04a/hpMsAuPm00by0oZBVeZWUW1r771m+C9dUz0mXqpElNzuO1lHRYCU8yNyzHB8/1OPpntjYWCorPRdsamhoIDAw0MsZcPToURYsWKDP4aWlpZGQkEB+fr7+nr/85S/U1NTwwAMPHPPrBwUFERkZ6fEhhBBiAFU7g5T4MR67XeXHKbERGLoJUPpVaCyk5ajt5moVZCx+GQKCiQoJ4LFrTuaPiyYfP0BxGXW6ej20Wt+VHhfKVGc+x+q8YyxcONCsjaq8GlQDt05cyb5njE8gyGwayDvrMz0OUmbOnMm6dev0zwsKCrBarcTGep9DTE1NpaWlRf+8sbGRmpoaUlJU7fv777/PQw89xFtvvaXnugghhPBTemWPZ/7gkWoVpHjtDtvfJi5Sr9Hp8IM3IfgEOqq61hByNUZzOmO8Wufmq7yK737tvrbvA2hvVlNvrqRhN658lPMmD77SY5ceBynz58/HYrHw3HPPAbBs2TLOOeccTCYTdXV12O1dO/ItXryYp59+mi+++IIjR45w++23M2HCBLKzs9m7dy+LFy/m0UcfJS0tjcbGRpqbm/vuyYQQQvQt98oeN66RFJ8FKTOXwKJ/wY8+hYgT/IWcPgcMJqgtUOv8OJ3p7Nny9YEq2u0Ob2cPLL0N/lVd2uAfrGgkv7KJAJNBD7AGox4HKWazmWeeeYY77riD+Ph4VqxYwf333w9ATEwMu3bt6nLOueeey/33389tt93GhAkTOHDgAG+++SYGg4H//Oc/NDU1cf311xMREUFERASTJk3quycTQgjRt/TKHs8gpcgVpMT5KEgxB8KM6yEy6cSvFRwJKTPUtttoypSUKOLCAmm02th8uPbEv86JspRCgXNKKvuKLoddCbNzRsd3nyQ8SPSq4+xFF11Efn4+zz//PHv37tWDCk3TmDZtWrfn/PjHP2b//v20tLSwbt06PWH24YcfRtM0j4/Dhw+f0MMIIYToJw67WrcHukz3+Hwkpa91M+VjNBo4fZwakVi13w+mfHLfBc0BabM9muO5fOpcXHGwVvW49HrtnpEjR7Jw4ULi4gZnprAQQojvoK5QlfaagiAqVd+tadrQDVIOrdZXeQY4Y4Ka8lm1zw+SZ498o15dXXfdVFha2VZYB6j+KIOZLDAohBDi+PSk2dGqgZtTZYMVq001ZUuOHuDKnv6SNlsFY41lHXk4wPyx8RgNkFfeQEldyzEu0M80DYo2qm1XZZMbV1XPtLRo/yqX/g4kSBFCCHF87mv2uHGNoiRHhxBgGiK/UgKCIX222i7oKEWODg3k5HTVF2yVL0uR646odYaMAZA8rcthfUHBQbhWT2dD5CdKCCFEv/JS2ePz8uP+ouelrPbY7RelyK5RlKSpXRrnWVrbWZdfBQzOVY87kyBFCCHE8emN3IZ40qxL1hnqteBrjxWWz3CWIn9zsAqrrWvrjQFRtEG9ps3ucmh1XiXtdo1RCWGMSQwf4Bvrez1uiy+EEGIYq3Z2Cu9U2eMqP04bakFK8nQIjIDWOtUmP2kqAJOTI0mMCKKiwcqmglrmGXeqQKY7sVkw/bouPUxOmB6kzOpy6Mt9aoTn3ImDf6oHJEgRQghxPG1NYClW23GjPQ65RlIyfNUjpb+YzJAxFw58oqp8nEGKwaBKkd/YcpSdOzYzL/cH4Gj3ehlr7HiCMruOeHxn1gYo36O2O42k2B0aq5zTUGc6K5EGO5nuEUIIcWyupNnQOLVWjpshO90DHev4dGqRrwIAjVl771UBStJUyLld/6jLXkKBphrLPf7ym32bv1K8RfVHiUrv0rxue1Edtc3tRASbmZHR/cK/g42MpAghhDg2PWnWc6qnpc1ORYMVGKJBiit59si3YGvTVxmeNzaeC00bOcW+Hc0UhOGK5XpDteY2G1c+9g3ft1fwE/N7JDbnc+Nzmzh/8gjuWTSZlBMt09ZLj7tO9XzlnOqZPy5hyFRaDY2nEEII0X9c+Sid2uEfrVWjKBHBZqJCBm/rda8SJ6vRo/YmKNmq7440tPKnoJcA2Jl5o0fH13tW7GF/eSPFQWpa7PSocsxGA5/sKeecB1fzxKp82mwnsPaPKx8lvWt/FFc+imudoaFAghQhhBDHVn388mNDXyeH+gOjETJPU9vuUz6r/06co4ojjkQeb1+k7359cxFvbjmK0QBXLlSdYFPbCvjozlOZlRVLS7ud+z/ex6JH19LQ6j2PxSuHA4o2qe1OIyll9a3kllowGBjUCwp2JkGKEEKIY/My3TOk81Fc3FvkA1TshfWPA/D/bNezuqCB1nY7eWUN3LNiNwC/PHcc06bOUF1r25sYF1DFazfn8NCVU4kJDSCvvIGVu8t6fy9VeWCth4AwNcrjxpUwm50aTXx40Hd7Vj8kQYoQQgjvNM1tumc4BinO5NmjG6GtGT78FThsaOMXsDd8Dq3tDr7cV8HtL22htd3B/HEJ3H7GGFUdlDhRnVu+B4PBwKUnp3LJdLXuUV5ZQ+/vxTXVkzpDXd+Na6rnrCE01QMSpAghhDiWxnJoawCDEWIyPQ4N2R4p7uJGQ2SKWlzxo1/BkbVgDsHwvfs5c4KaVvnVGzvIr2xiZGQwD185FaPROfU14iT1Wr5bv9yEpAgA9pVZen8vetKsZ+mx1WZn7UHVZfasIVJ67CJBihBCCO9cUz3RGWD2nEYYsj1S3BkMHVM+21WyLPN/BdHpevfZ5jY7JqOBR6+ZTpz7VMtIV5CyR981YaQKUk5oJKVTkLKxoIbmNjsJEUFMTo7s/XX9mAQpQgghvHP1SOk01aNp2vCY7oGOIAVU8vDcnwJw6ph4Ap2lvkvPH8/MTM8eMoxw5o2U7dJ3jU2MwGiAqsY2Kp3l2z3SVN3xvUg9xeNQR1VPQscozhAhfVKEEEJ452X144oGK1abA6NBrYA8pLnyUgAWPKCPKIUHmfnn1dMoqWvhR6dmdT3PNd1TdwRaLRAcSUigicy4MA5VNZFX1kBCRA+TXI86p3oSJkCIZ6M214rMQ22qByRIEUIIcSxeVj92jaIkR4cMmcZhXkWlwPcfA4cdRp/lcWjBlCQvJ6G680YkQ0OJqgpKV9M040dGcKiqiX1lFuaNje/ZPXhZr6egqomCqiYCTAZOHdPDaw0iQ/wnSwghxAnxMt1TWD1Mpnpcpv8AZlzf+/NcUz7lHVM+E0aqvJF9vclL8ZI065rqmZkZS0Tw0GuoJ0GKEEKI7tnaoPaw2vYykjJsgpTvqpvk2fEje1nhY29Xa/ZAlyDF1Qp/KE71gAQpQgghvKk9DJodAsMhwnNaY1iUH/cFV15KWUcZ8kRnGfKB8kZs9h60yC/bCbZWlYviFiw2Wm1sKKgGhs6qx51JkCKEEKJ7ejv80aoU142MpPSQK0ipyFVt7YG0mFBCA01YbQ4OO6fNjsl9qsft+7D2QBXtdo2MuFBGxYf19Z37BQlShBBCdE+v7Bnb5dCw6JHSF+LGqPb4bY1QdxgAo9HAuBG9mPLxkjT7lduCgkNy7SQkSBFCCOGNl8qeljY7Fc4eHzKSchwmMyROUNvfpambpkFh1yZumqbxVd7QzkcBCVKEEEJ446Wyp6hWjaJEBJuJChl6FSV9rpu8FFeQsrf0OEFK/VFVwmwwQfLJ+u49JRYqGqyEBpqYPSr2GBcY3KRPihBCiO55aeTmXn48VKcZ+lQ3a/iMd5Yh55U7p3taauHgF+CweZ5bukO9JmVDoBq1qrC08rt31bVOHRNPkNnUf/fuYxKkCCGE6KqhDJpUJ1PiRnsckqTZXtJ7pXSd7imqaaHRaiP83Z9A3ofer+Gc6tl1tJ6b/reZMksrUSEB3HlW13yhoUSCFCGEEF3tfku9ps6EoAiPQxKk9JJrJKW2AKwNEBRBTFggIyKDKLdYObJvG5PzPgQMMPpM9eouKBxm38oHO0v41Rs7aG13MCYxnGd+eAqZQ7Sqx0WCFCGEEF3teFW9Zl/V5ZD0SOmlsDjVZ6ahVLXHd1bpTBgZSbmlkqBNj6v3TVgIV7/U5XSHQ+Ofn+/nX1/mAnDG+AT+tXg6kUOww2xnkjgrhBDCU8Ve1UDMGAAnXdblsIykfAfdrIg8YWQECdSRWfy+2uFcXdldc5uN21/ayr++VPlBN52WxbPXzxwWAQpIkCKEEKIz1yjK2PPUInlumttsHJEgpfdGdG2PPyEpguvMn2LW2tW0WqeW9wB//ziPj/eUEWgy8sDl2fxu4SRMxuGTrCzTPUIIITo4HLDrDbU9tetUz/s7SmizOciIC5UgpTe6qfCZEGvmDNPnAGhz7uhSKdVotfHG5iIAHr1mOudPHjkw9+pHJEgRQgjR4fDXYCmG4CgYd0GXwy9vKARg8ax0jMPoL/oTpi806GyPbzQypnQFAYZGDjtGEJh0DsmdTnlnWzFNbXZGJYRx3qQRA37L/kCme4QQQnTY+Zp6nXwJmIM8Du0urmfH0XoCTAYun5Hqg5sbxOLGgCkQ2hqgvhAcdgI2qITZZ+wLyKvwXMNH0zReWn8EgGtnZwzbfjQSpAghhFDamiH3PbWdfXWXwy85R1HOnzyS+PCgLsfFMZgCIMHZHr9sN+z7AGoP02iK5E37fPZ2WsNn85Fa9pU1EBxg5PKTh29AKEGKEEIIJe8j9Zd+dDqk53gcarTaeG97MQDXzE73xd0Nfu55Kd/8C4C9KVfSSlCXNXxedI6ifH9qClGhw6OSpzsSpAghhFBcUz3ZV0Gn6YUV2535EfFhzBkV54ObGwJceSnbX4bizWAKovXkHwGwz20Nn6pGKx/tKgXgBzkZA36b/kSCFCGEENBYodaOgS5TPZqmeSTMDtf8iBPm6pVSp0ZJmHo1ozJHAZBf2UibzQHA65uLaLdrTE2LZkpqlC/u1G9IkCKEEEK1wdfskDID4j0XFNx5tJ49JRYCzUYuk4TZ78413eMy5w6So4KJCDZjc2jkVzZid2i8tF4FhD+QaTUJUoQQQuDWBr9rwqxrFGXBSSOJDQscyLsaWsLiIdzZ62Tc9yBhHAaDQV9scF+ZhdX7KyiuayEqJIBFUzsXJQ8/EqQIIcRwV5kHpdvBaO7SBt/S2s57O0oAuGb28M6P6BPjLwBzMMxfqu+aMDISgH1lDbywTk0FXTEjleAAk09u0Z9IMzchhBjuXAmzY85Vi+G5WbGtmJZ2O2MSw5mZGeODmxtiFj4M5/4FgiP1XeOdIylf7avgQEUjANcO84RZFwlShBBiOHM4YOfrartTG3xN0/TeKNdIwmzfMBo9AhSAiUkqSNlfrgKU08bGkxUfNuC35o9kukcIIYazkm1QXwRBkSpPws3Wwjr2lTUQZDZy2TBuKNbfxo2I8Ph8uJcdu5MgRQghhrOCVeo1az4EBHscciXMLsxOGtYNxfpbRHAAqTEhACRFBXP2hEQf35H/kCBFCCGGs4I16jXrdI/dTVYbH+xUCbPXSsJsv8t29kNZPCsds0l+NbtITooQQgxXNisUrlfbWfM9Dm0vqsNqc5AUFczJ6dEDf2/DzG8XTGTOqDiumim9UdxJkCKEEMNV0UawtUL4CEgY73Fo8+FaAE7JjJWE2QGQGhPKdXMyfX0bfkfGlIQQYrjSp3rmd1mrZ0uhM0jJkLJj4TsSpAghxHDlHqS4sTs0th1RQcoMCVKED0mQIoQQw5G1Ua3EC12ClP3lDTRYbYQGmvSW7UL4ggQpQggxHBWuA4cNojMgJtPj0GbnKMr09GipNBE+JT99QggxHBWsVq+dRlEAthyuAWBGRuxA3pEQXUiQIoQQw5GX/iggSbPCf0iQIoQQw01zDZTuVNudRlIqLK0U1bRgMMA06Y8ifEyCFCGEGG4OrwU0SJgAESM8DrnyUcaPiCAyWFrhC9/qdZCye/duZs6cSUxMDEuXLkXTtB6d53A4mDt3Lg8++KDH/jfffJOMjAySk5N55ZVXens7QgghestL6TG4N3GTqR7he70KUqxWK4sWLWLGjBls3ryZ3Nxcli9f3qNzn3zySerr67nzzjv1fbt37+baa6/lD3/4A5988gn33HMPeXl5vXoAIYQQvaQnzR4rH0WSZoXv9SpIWblyJfX19Tz00EOMHj2aZcuW8eyzzx73vJKSEn7729/y6KOPEhDQMXz4zDPPcOaZZ7JkyRKmTJnCHXfcwQsvvND7pxBCCNEzllKo2g8YIPNUj0MtbXb2FNcD0sRN+IdeBSk7duwgJyeH0NBQALKzs8nNzT3ueT//+c/JyMigqKiIb7/91uN6Z511lv75rFmz2LJlS7fXsFqtWCwWjw8hhBC9dPhr9Zo0FUI8A5EdR+uwOTQSI4JIjQnxwc0J4alXQYrFYiErK0v/3GAwYDKZqK2t9XrOunXreOONN0hNTSU/P5/rr7+eO+64o9vrRUZGUlJS0u117r33XqKiovSPtLS03ty6EEIIOHZ/lCMd+SiyqKDwB70KUsxmM0FBQR77goODaW5u9nrO008/zezZs/nggw/485//zJdffsnjjz9OXl5el+sd61p333039fX1+kdRUVFvbl0IIYSmwSFn0uyobvJR9PV6JB9F+Adzb94cGxvL7t27PfY1NDQQGBjo9ZyjR4+yYMECPSpPS0sjISGB/Px8YmNjqays7NG1goKCugRIQggheqH2MNQXgtEM6XM8DjkcWsdIiuSjCD/Rq5GUmTNnsm7dOv3zgoICrFYrsbHeo+7U1FRaWlr0zxsbG6mpqSElJaXL9bZt20ZKSkpvbkkIIURPuUqPU2dCYJjHofzKRupb2gkOMDIpOdIHNydEV70KUubPn4/FYuG5554DYNmyZZxzzjmYTCbq6uqw2+1dzlm8eDFPP/00X3zxBUeOHOH2229nwoQJZGdnc9lll/Hqq6+ya9cuGhsb+de//sX555/fN08mhBDC0zFKj11N3KamRhMgiwoKP9HrnJRnnnmGO+64g/j4eFasWMH9998PQExMDLt27epyzrnnnsv999/PbbfdxoQJEzhw4ABvvvkmBoOBqVOn8rOf/YxTTjmFlJQUTCYTt99+e988mRBCiA6adswmbu5Js0L4C4PW05axbsrKytiyZQs5OTnExcWd8E3k5uZSXFzM6aeffsz8FncWi4WoqCjq6+uJjJShSSGEOKbyPfDEXDCHwF1HwOyZ43fmP1ZRUNXEczfM5MwJiT66STEc9Ob3d68SZ11GjhzJwoULv9PNdWfSpElMmjSpz64nhBCikz3vqNdRp3cJUKoarRRUNQFwcrqMpAj/IROPQggx1DkcsPM1tZ19ZZfDrqmecSPCiQqVRQWF/5AgRQghhrqi9VBXCIERMH5Bl8Md/VFkFEX4FwlShBBiqNvxqnqd9H0I6NruXpq4CX8lQYoQYtBwODQ2HKqmtb1ruwPhRXsr7HlXbU+9qsvh1nY7u46qRQWliZvwNxKkCCH8T1sTFG1UuRRufvfubq76z3ruW7nPRzc2CO3/GKz1EJkKGfO6HN5aWEub3UF8eBAZcaE+uEEhvJMgRQjhPxx22PI8/OtkePZc+PYR/dCrGwt5ZWMhAO9sK6bN5vB2FeFOT5i9Aoxd/5f/5d4KAM4YnyCLCgq/I0GKEML3NA32fwpPnArv3wmNZWr/luWgaWwvquOeFXsAMBqgvqWdVXkVJ/Ql95VZqGywnuCN+7mmajjwqdrOvrrbt3y5T/07ni29UYQfkiBFCOFbpTvgf9+Hl6+Ayr0QHA3n/AkCw6H2MHX713Lbi1toszs4d9IIbpibBcCK7SXf6cs5HBoPfZrHBf/8mh88s4Hv0M9y8NjzNjhskDQVEid0OXyospFDVU0EmAzMGxvvgxsU4tgkSBFC+M6Gp+Cp09WaMqZAmHMH/Gw7zPs5TFwEwKYVT1Ba38qo+DAevHIql0xXi5B+vrechtb2Y1+/dAc8egp8dS8AzW02bn9pK//68iAAeeUN5Fc29dfT9a+jm+GhybDuce/v0ad6jj2KMjsrjohg6Y8i/I8EKUII36gpgM/uATSYfCncsQnO/xuEOCtMslUlysymVUQFOnjquhlEBgdwUkokoxLCsNocfLy77BjXPwQvXg7VB+CbRyipqOSyJ9bx8Z4yAk1GUmNUKe4Xe8v7+UH7yTePgOUofHI3bP1f1+PV+XB0ExiMcNJl3V7iC2c+ylky1SP8lAQpQgjf+PgusLXCqDPg8v9CTKbH4Q8bx1KmxRBtaOK/c2sZOyICAIPBwCXT1GiK1ymfxgp44VJocuat2Fr4z5MPs7fUQnx4IK/cPJub548C4It9J5bb4hMttapqx+X9n8G+jzzf4xpFGX0WRIzocon6lnY2Ha4B4OyJEqQI/yRBihBi4O37SP2SNQbA9x6ATlUl+8sbWPrWbt61nwrAjLpPPI5/3xmkfJtfRYWl1fParRZ48TKoLYDoDA5mXgPAue2rmJQUyYo75jEjI5Yzx6tfzFuO1FLX3NYfT9l/9rwL9jZInAzTfgCaA968EY6sU8c17bhTPV8fqMTm0BidEEZGXNjA3LcQvSRBihBiYLU1w8rfqO25d0DCOI/DFQ2t3PjcJprb7BQkOxcy3f8JNNfo70mPC+Xk9GgcGry3w200xWaF134AZTshNJ41s5/ihrzZAMwx5fLWtemkRKtpnrTYUMaPiMDu0Fi9v7L/nrc/uAKQqVfBokdg3AVqVOqVq6A8l6M7V0HtYZV8PKH7xWBdpcfnTOw6yiKEv5AgRQgxsL5+EOoLISoN5i/1ONTSZuem5zdTXNdCZlwov7n+chhxEjjaIfddj/dePL3TlI/DAe/copJwA8Opu/QVfvF5I0e1BA6HT8OIRsi+tz2ucZZzmuPLwTTlU3sYCtcBBphyBZjMcPlzkDYbWuuxLr+Y3Df/ot478SII7Nqgze7Q+CpP8lGE/5MgRQgxcKoOwrf/UtsX3AuBHdMMdofGz17dxo6j9USHBvDcjbOIDQvUE2jZ8ZrHpRZOScJkNLCruJ78igaV47LnHTWFdNWL/G6DmeqmNiaMjCDl9Bs6ruFWcuzqDbIqrxKbfZA0h9v5hnrNmg+RyWo7MBQWvwoJEwhqKec80xYAdsVf0O0lthfVUtvcTmSwWRYVFH5NghQhxMDQNFi5VOVSjDkXJlzocXjZR3v5NLecQJORp394ClnxzgBmyhWqQqVovaoIcooLD2K+s7dH6Qd/g41PAQa49Ck+aBrPh7tKMRsN/OOKqQScdAmYglQflrJd+jWmp8cQHRpAfUs7Wwvr+vtf4MRpGux0LhY4tVOuSWgsjVe8TokWB0CpFsvdW6JwOLr2gflC7zKbiNkkvwaE/5KfTiHEwMhdAflfqmBhwd89kmX/t+4wz65VAcgDV2QzM9NtNd7IJMg6XW3vfN3jkhdPT+Eq01fMK3xC7fje/VRmXMgf3t0NwE/OHMNJKVEQEg3jnaMKOztGZExGg55A+8W+QVCKXLwVqg+COUTvI+PukyIT17XdxSbjVP5p+AG7y5p4Z1txl/fpXWalqkf4OQlShBD9z9oIH9+ttuf9HGJH6Ye+2FvO/3tPtbxfev54vXLHg2vKZ+erHtM1F5i3ssz8DACl2bejzbqZ37+7i9rmdiYlRfKTM8e4XcM58rDrDbDb9N2unAxXIqlfc42iTLwQgiK6HF6xo4R8LYVv5z5L5hk3APDgp3keq0YfrW1mX1kDRgOcPi5hIO5aiO9MghQhRP9b+xA0lEB0Bsz7hb47t8TCT1/ZhkODq05J4/YzRnd//sRFEBCqGrQd3az2HVlH0LtLMBk0XrOdwRPGa3hvRwmf7CnXp3kCzW7/ixtzDoTEQmM5FKzSd88fl4DJaOBARSOF1c398PB9xN4Ou99S292UFVc3WvnmYBUAF01L5sZTM0mOCqakvpXnvjmsv+8r5yjKKRmxRIcG9vttC3EiJEgRQvS/A5+p17N+DwEh+u4nV+fT3Gbn1DFx/PWSk7yvwhsU3pHDsvM1KM9V5ba2VqpSzuK3th/z/s5SfRHCO88ey6TkSM9rmAM7Oq+6JeFGhQQwM1Mlj/r1lM/Bz6G5GsISVQO8Tj7aVYrdoZGdGkVWfBjBASZ+df54AB7/6iA1TaoXjKt53Vky1SMGAQlShBD9z+LMi0ic5LG7sEaNXFyXk0nA8RI4pzqnfHa/CS9eCq31kJZD9HUvEB0WQm1zO/Ut7ZyUEslt3kZkXMmm+z5QU1BOZ09QvUL8uhTZlUsz5XJVdtyJq1/MRVOT9X0XT0thUlIkDVYb//riAM1tNr7NrwZk1WMxOEiQIoToX+0tagQAIMoz36Tc2S02KSr4+NfJOgPCR6iW8A2lkDARrnkVc3A4F2YnARBgMvDgFdO8BzwpMyB2NLQ3q0DFyTWqsP5QNY1WW/fn+lJrfUfbe1d+jpviuhY2Ha7FYIALszuCFKPRwG8XTATgxfVHeGl9IW02B2mxIYxJDB+QWxfiREiQIoToXxZns7WAMAiO1nfb7A4qGqxAD4MUk1mVI4NqBHfd2/pihD+eN4qpqVH87ZIpjB/ZNaFUZzB0jKbseFXfPSo+jMy4UNrtGmsP+GH32dwVYLdCwgRImtrl8PvOUZTZWbGM7PRvOW9sPKePS8Dm0Lh35V5AjRx5nVoTwo9IkCKE6F/1R9VrVIpH2XFVYxt2h4bZaCAuPKhn1zr913DWH+CGDzsamaHa5K+4Yx5XnpJ2/Gu4Ap2C1WApBdSihWc5p3y+8McqH1cOTfZVXdY5Anhvu2uqp5vKKODuBRMwGsDVMkVKj8VgIUGKEKJ/ufJRIj1/gZbWtwCQGBGEydjDv+qDo2D+ryAm47vfT2wWpOWoRfl2vaHvPsf5i/urvIpuG6D5TF0hHFmrtl0BlpuDFQ3kllowGw1876SR3V5iwshILp+RCkBYoIlZWbHdvk8IfyNBihCif9U7g5RO+Shl9SofpfP0xIBwT8J1OiUzloggM1WNbewsrh/4e/LGVXaceRpEdx0pco2izB+XQEyY95LiX503npmZMdxx1liCzKZ+uVUh+poEKUKI/mVxTvdEpnrsLq13Jc2GdD6j/7lKeCv3683hAs1G5jubm325149KkfO/VK+Tvt/lkKZp3Vb1dCcxMpg3bp3rvfJJCD8kQYoQon95GUlxVfb4ZCQlQlUDYWuB1jp9t6v77Of+kpfS3gKFG9S2a2kAN7uK6zlc3UxwgJFzJ40Y4JsTov9JkCKE6F9ec1J6UX7c1wJC9MogvfoIOHWMWrAwt9RCuz+sily0UVX1RCRB/Nguh1c4p3rOmTiCsKCuvVOEGOwkSBFC9C99JMVzuseVkzIi0gdBCnQETc4KH/BM4q1ubPPFXXkqWKNes+Z3qeqxOzQ+2NmzqR4hBisJUoQQ/cfaAFZnEmrnkRSLqu7xyUgKdEz5NHSMpBiNBuLDVfJpRUOrL+7Kk3uQ0snGghrKLVYig82cPl4WChRDkwQpQoj+4xpFCY5S6+84aZpGeb1q5OaTnBSASGeQ4jaSApAYoe6nwmId6Dvy1GqB4i1qu5sgZe1B1XTunEkjpFpHDFkSpAgh+o+Xyp6apjba7A4Mho6gYMDp0z3FHrsTIlRjucpGHwcphetAs0NMFkSndzm8q9gCwPT0mIG+MyEGjAQpQoj+46Wyx5U0Gx8eRKDZR/8b0qd7Oo+kqCDF5yMpx5jq0TSNPc5eLlNSogbyroQYUBKkCCH6j6slfqSXRm6+SpqFjrb6naZ7OkZSfJyTcmi1eh3VtfS4tL6V6qY2TEYDE461VpEQg5wEKUKI/mPxMpLiyx4pLq6RlE7TPX4xktJUDeW71HbmaV0O73aOooxNDCc4QPJRxNAlQYoQov/Ud5+TUlbv48oe6BhJaamB9o5RkwRnjoxPc1IOf61eEydBeNfFAF1Bykky1SOGOAlShBD9x8tISpmvK3tANXMzO7++W15Kgj+MpBQ4p3q66TILsLtEJc1KPooY6iRIEUL0D03rSJztnJPi6x4poJqj6VM+Hb1SXNM9lQ1WNM1HqyEfI2kW3EdSIgfqjoTwCQlShBD9o6VWrY0DXlvi+6zbrIvrvroZSWmzO7C02Ab+nuqLofogGIyQMbfL4QpLKxUNVowGmJgkQYoY2iRIEUL0D1c+Smg8BHQEI5qm6dU9PlkB2V1k15GU4AATkcFqHRyfdJ11jaIkT4eQ6C6Hd5eoUZTRCeGEBsp6PWJokyBFCNE/vOSjWFptNLfZAR+XIIPXXikJblM+A+44Uz27jko+ihg+JEgRQvQPL5U95c7y4+jQAEICfVw+66XrrN4af6CDFE07fj6KcyRlsgQpYhiQIEUI0T+89Ujxh0ZuLt7W74n00UhKzSG1lIApENJyun3Lbuk0K4YRCVKEEP3DW2WPs0eKT8uPXSKcvVI6T/eEO8uQBzonxVV6nDoLAkO7HK5qtFJa34rBAJOSJWlWDH0SpAgh+oc+kuI53VOqJ836QZAS6ZaT4nDou302ktLD0uOs+DDCgyRpVgx9EqQIIfrHcdft8XFlD0D4CFXq67BBU6W+W2/oNpBBisPREaR0s14PwB5nE7eTkmWqRwwPEqQIIfqew9FR1uslJ8UvRlJMARDmbDvf4N7QzQeJsxW50FwNAWGQfHK3b5F8FDHcSJAihOh7TZXgaAfcuro6lfvD4oLuuumV4pMSZNcoSsYcMAd2+5Zdxa7KHslHEcODBClCiL5ncU71RIxUoxVu9OoevwlSXGXIXVvj17e009pu7/970DTY/abaHnVGt2+pa27jaK1KOp4s0z1imJAgRQjR97xU9jS32ahvaQf8KEjppqFbVEgAgSb1v8eqgVgNuXAdFG9RCx5mX93tW3YXq3yUjLhQokICun2PEEONBClCiL7ndfVjNYoSFmgiwl+qU7qZ7jEYDAObPPvto+p16mIIT+j2La4mbidJPooYRiRIEUL0PS/dZsvcpnoMBsNA31X3upnugQHMS6k6AHkfAQaY8xOvb3Plo0hljxhOJEgRQvQ9byMpFj9ZWNDdcdbv6feRlHX/Vq/jF0D8WK9v2yOVPWIYkiBFCNH3vOSkuJJmR/hDS3yXSGfX2c6t8V0jKZZ+7DrbWAnbX1Hbc3/q9W2W1nYOVzcDMFk6zYphpFdByu7du5k5cyYxMTEsXboUTdOOe052djYGg0H/WLJkCaCWa7/tttuIjY0lOjqaG264gZaWlu/2FEII/+Kl22yZP/VIcXGNpLQ1QKtF361P9/Rn4uzG/4DdCimnQHr3a/UA7HEmzabGhBAT1n15shBDUY+DFKvVyqJFi5gxYwabN28mNzeX5cuXH/Oc5uZm8vPzqaiooLa2ltraWh59VCWIvfDCC+Tl5bFt2za+/vpr9uzZw7333ntCDyOE8AN2W8fUiZeRFL+p7AEICocg5xSK25SP3tDN0k9BSlszbHpabc/9KUdqmnnk8wN6Hxl3e0okH0UMTz0OUlauXEl9fT0PPfQQo0ePZtmyZTz77LPHPGfbtm1kZ2eTkJBAdHQ00dHRhISoueiNGzdy+eWXk5GRwZQpU7j44os5ePDgiT2NEML3GstAc4DRDOGJHofKLGq01K9GUqDbCp/E/h5J2f4StNRCdAZMXMQjnx/g4c/3s/BfX/NtfpXHW11Js1NSJUgRw0uPg5QdO3aQk5NDaKhamTM7O5vc3NxjnrNx40aOHj2qBym33XYbVqv6D37y5Mm8+OKLlJeXc+TIEV599VXOPfdcr9eyWq1YLBaPDyGEH3Llo0Qkg9HkcaisXv3371cjKdAx5dNN19l+GUlx2GHdY2p7zh1gNJFf1QRAVWMbP3hmA4+vOojDoabUXe3wJR9FDDc9DlIsFgtZWVn65waDAZPJRG1trddz8vLymDdvHmvXruWTTz7hs88+4+GHHwZgyZIlNDY2MnLkSDIzM8nKyuL666/3eq17772XqKgo/SMtLa2nty6EGEiubrOdKnvabA69MZpfVfdAx7SU+/o9zpWQqxqterDQZ/Z9CLUFEBID068FoLBaBSlzR8fh0ODvH+dx8wtbKK1v4ZAzgJEeKWK46XGQYjabCQoK8tgXHBxMc3Oz13OefPJJXnnlFcaPH8/s2bO55557ePNN1fr5kUceITo6miNHjlBYWIjNZmPp0qVer3X33XdTX1+vfxQVFfX01oUQA8lLZY8r1yLQbCQm1M86purTPR05KXFh6v93NodGbXNb3349V/O2mUsgMAxLazu1zaoT739+eAr3XjqFQJORz/eWc8E/v0bT1BRZfHjQMS4qxNDT4yAlNjaWyspKj30NDQ0EBvY80zwxMZHiYvU/sJdeeomlS5eSnp5OWloa99577zFzXIKCgoiMjPT4EEL4oeP0SBkZ6UeN3Fy6me4JNBuJdVbS9GmvlMINcHQjmAJh1s0AFNWoP/biwgIJDzKzeFY6b902l9SYEH0ZAVmvRwxHPQ5SZs6cybp16/TPCwoKsFqtxMbGej1nzpw5HiMe69atIyMjAwCHw0FFRYV+rKysDLt9ABbyEkL0Ly/dZv2ysselm+kegITwfug666romXq1nlhc6OyBkhYbqr9tSmoUH/x0HmeOV23yTx8X33f3IMQg0ePFM+bPn4/FYuG5557jxhtvZNmyZZxzzjmYTCbq6uqIiIjAZPJMkps8eTK33HILf/zjH9m3bx8PPvggjz2mksVOO+007rvvPkwmE21tbdx///1cdNFFfft0QoiB52Ukpdwfe6S4dDPdAyovJa+8oW9HUsr3qNdJ39d3FTpHUjLiQj3eGh0ayH9vmElxXQsp0X6WxyPEAOhxkGI2m3nmmWdYvHgxS5cuxWg0smrVKgBiYmLYtm0b06ZN8zjnH//4BzfeeCNnnnkmiYmJPPDAA3py7F//+lcsFgu//vWvaWho4Pzzz+eRRx7pswcTQvjIcbrN+uVISoSz62xTJdjawKymefp8/R5Ng9rDajumoxDBFaSkx4Z2OcVgMJAa03W/EMNBr5Yhveiii8jPz2fLli3k5OQQFxcH4LXzbHR0NO+8847XY//73/96ebtCCL9ms0KTcxq3c7dZZ4+Ukf7UEt8lNA6MAeBoV31eotMB9/V7+qg1flMVtDcDBo9/H1eQktZNkCLEcNbrtXtGjhzJwoUL9QBFCCF0rsRTc7D6xe+m1J+ne4zGbqd8XF1n+2wkpe6Ieo1MBnNHpY4+3SNBihAeZIFBIUTfceWjRCZDpwqeMn26x09zK1xTPg3dNHTrqyDFNdUTnaHvstkdFNeqUab0OAlShHAnQYoQou94yUexOzT9F71fjqTAMVvjV/X1SEpMR5BSWt+KzaERaDYyIsJP/22E8BEJUoQQfUfvNuuZj1LVaMXu0DAZDf7bkMwVWHXXGr/PRlKcQYrbSIqejxITgtHoZ/1jhPAxCVKEEH3nOJU9iRFBmPz1F7GroZvHSsgqSGm02mhus53419BHUjL1XUeqXeXHYSd+fSGGGAlShBDe2azqo6e8dZutd1b2+OtUD3Q73RMeZCYkQPV/6pPkWb38uOtISnflx0IMdxKkCCG611oPj82Gf8+EljqPQ498foC73tpJu93RsdPhgKoDattLt1m/zUeBbqd7DAZD3035OOwd3Xg9pnvU4oFSfixEVxKkCCG699UytVJv3RH46m/67o92lfLw5/t5dVMRL28o7Hj/jpehJh8CwyHlZI9L6ZU9kX5a2QOe0z1uvZ8S+6qhm6UYHDa1Zo/rayHlx0IciwQpQoiuSnfCxv90fL7pGSjZTlWjld+/u1vf/cgXB2hobYfmGvjsHrXzjLsgzHOdGdfign49kuIKHOxt0Fyt79ZHUiwn2NDNlTQblab6sji51u2R8mMhupIgRQjhyeGAD/8PNAdMvgQmXwqaA+3D/+Oed3ZS09TGhJERjEoIo6apjSdX58OXf1G/2BMmwuxbPS7XaLWxqaAGgKRoPw5SzIEQ6gyuuilDrmw8wZGUbsqP65rbsLSqhNw0aX0vRBcSpAghPG1/CY5uVNM25y+D8/8GgeEYijcTvu81zEYD/7hiKr+5YAIAG77+HG3zc+rchf8AU4DH5f78/h5K6ltJiQ7hjPGJA/00vRPpaujWUeHTMZJygkHKMcqPEyOCCAk0dXeWEMOaBCnCb3lbE0r0o+Ya+PyPavuMu9Qv7chkGucsBeAu8yv837x4TkqJ4rxJI5idEckfjM9iQIPsqyBznsflPtlTxuubj2IwwINXTiU8qFfLhQ08V5DiMZKiRn9OOHFWr+zJ1He5yo+lskeI7kmQInyr1QJ5H6vKBzcrd5Uy5ncreWvLUR/d2DDVzbSNpmksLcphnyONWEMjN7e/CKjKlwdG72Cq8RAWLYT9U3/tcamKhlbufnsXADefNoqcUYNgva+IrmXICZF9lDjbzXSPXn4s+ShCdEuCFOFbn/8RXrkKvn7QY/cL649gd2jc//E+rDa7l5NFnyreAt1M27y3o4SVudX8yfEjAEzb/gdHt0BTFelbHwDgIdsV/HV1jX4pTdO4661d1DS1MTEpkl+eN25gn+W7cpUhu6/fE95HJcjdTPcUSY8UIY5JghThO5oG+z9V2xuehDb1P+z6lnY2OhMtKxqsvLWl2Fd3OHw47CpZttO0TYWllXtW7AFg7pmLIPtq9Z4Pf6mqeVrraIufzKucx5r9lXx9oBKAlzcW8uW+CgJNRv551TSCzIMk36K7lZCdIyk1Taq1f099llvOn9/Ppc3mgPYWaCxTB7rtNitBihDdkSBF+E7NoY61XpqrYccrAKzeX4nNoemL6D65Oh+be9Mw0fe2LIeSbRAUCef+Rd/923d2U9/SzpSUKG49YzSc9xcIioLS7SrBFgj8/j9ZnDMKgGUf7SO/spG/frAXgF9fMJ7xIyMG+mm+u26me+LCgjAawKFBdQ8rfDRN47fv7OK/3xTw0a5SqCtSBwIjICRGf590mxXi2CRIEb5TsFq9Gp3JlOseA4edz3PLAbh+TiaxYYEU1jTz4a5SLxcRJ8zaAF/8WW2f+TuIGAGoqYjP95ZjclbzBJiMEJ4IZ/2+49zpP4C0Wfz0rLFEBJnZW2rhyifX0dJuZ86oOH50apYPHugEuHeddSZum4wG4no55XOoqknPYVm9v9IzH8UZfbfZHJQ6lwuQbrNCdE+CFOE7BWvUa87tEBwFNfnY9n7EqrwKAC7MTuJHp2YC8NhXB3H0Yqhd9MLBz6G1Tk1DzFyi717jnLo5OT3aczRk5o8h63SIHQXn/AmA2LBAbj9zDADVTW1EBJt58Mqpg29V35gMMAeDtR7K9+i7XXkpPU2e3XCoIz9nzf5KHDUF6hO3fJTiuhYcGoQEmPTrCyE8SZAifMPhgIKv1fb4BXDKjwFoXvUwllYbsWGBTE+P4bo5mYQHmdlf3sjne8t9eMNDWN7H6nXChWDqKBH+en8VAKeNTfB8v9EE178Hd27z6Cx746mZpESrtvd/vfgkkqP9uAW+NwEhMOoMtb1/pb7blZdS0dCzrrPrD3V0rK1uaqP66H71iVs+ivtUj8EwyII5IQaIBCnCNypyobkKAkIhZQbMvgWMAURWbuFkw37OHJ+IyWggKiSA6+aovz4fW5UvvVP6mt0GBz5R2+MX6Lttdgff5KsgZf64hO7O7CI4wMTrt87htZtz+P60lOOf4K/GXaBe89yClGOt37P7LVjzgD49pGmaHqS4zqsvOaje615+XK0WFpTyYyG8kyBF+IZrqidjrmpHHjESLftKAJaYP+KciR2dSX90ahZBZiM7iur4Nr+6u6uJ7+roRmipheBoSJut795xtI6GVhtRIQFMSYnq8eVSokOYPRj6oRyLK0gp3gINavTO60rILbXwzq3w5V+hcD0Ah6ubqWiwEmgycuvpowEw1jsXYuym26wkzQrhnQQpwjdcQUrWfH1X0QTVh+MC4ybmJzTq+xMigrh6ZhqgclNEH8r7SL2OPc9jqmeNc6pn3ph4TIMtr+RERSZB8nS17RxlcnWd7TKSsuddtSAh6IngrlGUaenRnH/SSADi2p2J3901cpMgRQivJEgRA89ugyPfqG23IOWj8hi+sk/FaNAI2/KUxyk3nz4as9HAt/nVbC2sHci7Hdpc+Sjjv+ex29XvZP64+M5nDA+uqS/nlI/XkZSdr3VsOwPvDc4gJScrlpToEKYnQJRBBSREp+tvPyKrHwtxXBKkiIFXuh2sFjXFMDJb3/3F3nL+Y79QfbLtRbWOjFNKdAiXTFd5Do9/lT+ANzuEVR2E6gOqBHzM2fru+uZ2thfVATCvc9LscOGa8sn/Ctpbus9JqT0Mhes6Pi/aiNbWxHpnZY9rGYAL09RIS4MpBgLDAJW3It1mhTg+CVLEwHP1R8mcpypFgJqmNrYcqWWdYxJtidlga4FNz3qcdusZozEY4PO95ewrswz0XQ89ruqVjFNVCbjTt/lVODQYnRCmV+sMOyOnQGSq+jksWOM2ktLakby983X1mnU6RKWBo52KPasos7QSYDIwPV01bZsbpxJkDzvi9XOrm9poarNjMEBqzDD9NxaiByRIEQPvkDNIyTpd3/XVvgocGkxMiiLwtJ+pnRufgvaOks/RCeEsOEl1BH3+28MDdbdDlz7Vs8Bj95oDvavqGZIMBhjvqvL5SM9JaW13UG6xqkqeHa+q41Ov1qcta3Z/DsC0tGhCAlUAPjZQ/XsesiWQW6qCa1c+SlJk8OBZMkAIH5AgRQys9lYo2qC2R3UEKV/sU1UU505MhEnfV3+ZNlXCrtc9Tr/8lFQAqfI5Uc01HVMVrl/GqGmINfud+SjDdarHZZwzT2f/J4QEGDk5PRqAVzcVqsqfmnwwh8DERXrAHVascq3cV3w2W1RL/CItQXWfpWNhQek0K8SxSZAiBtbRTWBrhfAREK9WxrXa7KzOU//zPnviCLX67kzV3I3db3ucPiMjBqNBJR2W1fessZboxsHPQbND4iSPBmMFVU0U17UQaDIye1Ss7+7PH2TOg4AwaCiF0u3c4Gzx/+L6QuzbnaMoEy+EoAjIOg2AlNYDRNLI7Cy3Mmzn6sdFWiKrnD/nsrCgED0jQYoYWO6lx84umxsO1dDUZichIqijJ8eERer18Fpo7cg/iQwOYFJyJAAbD3ck1opecjUqG3eBx+6vnVM9p2TGEBpo7nzW8BIQDGPOUtt5K/neSSMZERlEXWMTtp1vqv3ZV6vXyGTao0djwsFccx4nZ0R3XKfOFaQksOVILZbWdik/FqKHJEgRA6ugaz6Kq939ORMTO9Z6iR8DcWPA0Q75X3hcYlam+it1Y4FM+XwntjY46Pw39VJ63KUV/nDlmvLJW0mAych1ORmcbtxBUFstWlhiRwt94EjUTAAWRRzoCPAcDn0kxRCdgd2h8e3BKgr18uOwAXsUIQYjCVLEwLE2qLl80BMNNU3ji71qQcGzJ4zwfL/rF6grwdNpVpaahthYICMp30nht2oBvbAEtSSBU5vNwTpnrs+w7Y/S2bjzAQOU7YT6YhbPSucys8o7qchc5NEAb619EgCz2N1xfmM52K1gMDJ+/EQAVuVVykiKED0kQYoYOIXrwWFTrcGdnTf3lTVQXNdCkNnIqWM6/WJ0/RV74FPVAM5pZqYq7dxf3khNU9uA3PqQ4gr6xp6vl4ADbC2spanNTnx4IBNHRvro5vxMWDykzVLb+1cSZ2rhXNNWAJ5rmO3x1tcq1c90QsshaFSBt2uqh8hU5k9QlWlf7KugzKLyqSRIEeLYJEgRA+fQKvXqXtXjnOo5bWy8XrKpS5utGr611Kg1ZpziwoMYmxgOwCb3vJSaAo+SZdENTetohT++cz6KmuqZNya+Y9pNuC04+DHkriBAayPPkcrTB8MprW8BVLXO3voAch3Otveu3CvnVA8xGeSMiiPIbNQbwkUEmYkJDRjIJxFi0JEgRQwcPWm2I0jRO5t2HkUBNZQ+7ny17bYiLXQz5XPgc/jXdHjn5j695SGncp/6694UBKPO9DjkWq9nWPdH6Y6rj0zBGtiyHIDNUedid8CL61UQ4lqv50CYc/rMlXtV1xGkBAeYPEqT02JDMRgkGBTiWCRIEQOjuQbKdqntzNP03fmVqhvnuBER3Z/n+it2f/d5KfpIypoHAA1yV0DF3j677SHHFexlzYegcH13daOV3SX1gJeAcThLGK/KtO1WKNkKGEg57YcAvLyhkNZ2OxucwXJb+jx1TueRlOhMAE53CwCl/FiI45MgRQyMw18DGiRMgAiVIGu12fUEwtGJ4d2fN+ZsMAZA1X6o7lizxxWk7C6upzl/HRSt7zhn3b/1zbrmNhb/Zz3//vJA3z7PYOUKUjpN9XyTX42mwYSRESRGBvvgxvyYwdCRHwWQOY95M6aREh1CbXM7720v0UdSkrLPUmsh1R5WAUrtYXWOMwfrjPEdQYrkowhxfBKkiIHRzVRPYXUzdodGeJBZX8Cti+AoyDxVbbtN+SRFhZAWG4JDg6ZVD6udydPV687XoaEMUMPx6w5V8+Bn+8kra+jTRxp0GspVMz3w/KULepfZ02Wqp3vupdpTr8ZsMvLDOSrweOSLAxytbcFkNDB9TFpHxVTBmo7pnmj13qz4MNJi1Vo90m1WiOOTIEUMjKOb1WvGXH1XfmUjoBayO+bcvFuvCnezMuNIN5QTX/Sp2nHxEyrZ1t4GG/+Dze7g5Q2FgMoXfeCTvL55lsHqy78AGqTOhKgUfbemadIf5Xgy5kLsKIhIgokXAXDVzDSCA4wU16nk2SkpUYQFmfXyeg5+DpZite0cSTEYDPz6/AnMGxPPgilJA/4YQgw2EqSI/me3qYRNUKvLOrnyUUYneJnqcXFNTRSug5ZafffsrFh+bPoIAxqMORcSJ8Lcn6qDm55l9e4CSupbiQg2YzIa+HxvOZuHa5faoo2w7QW1fd5fPQ7tKbFQbrESHGDkFGd5t+jEFAC3fA0/2QDBqjw7OjSQS09O1d+iJ8W6gpS8laA5wBysloFwWjQ1mReXzCY2LHDAbl+IwUqCFNH/avLVej0BYRCTpe/Or3COpHjLR3GJyVRrzGh2VcXjlDMSrjSpKoq22XeoneMXQOxoaK2jZNWzAFwzK50rZqhfJvd/vA9N0/rowQYJhx0+/KXannYtpOd4HH5vRwmgmukFB8iKvF4FhavpRzc3zM3Ut/W1jlJnqcDErkqNic7Ql4AQQvSOBCmi/5U7O3COmATGjh859+me49J7VXyk70o79AohhjZ2OTLZajxJ7TSaYM5PADi9+nXMBjvXzE7nZ+eMJchsZNPhWr7KqzjxZxpMNj2rKquCo+CcP3kccjg03ncGKYumJvvi7ga1cSMiuPX00Zw7aQRzRztHUgKC1bSji3OqRwjRexKkiP5X5gpSJuu7NE3r+XQPdPSqOPiFWnumvRXDxv8A8LTtQjYe7pgGYupims3RpBsr+XlKHhlxYSRFheh/9f794zzsjmEymtJYAV86p3fOvgfCPXNONh+ppbS+lYggs0fliei5u743gad/eApBZrdRKNeUD+hJs0KI3pMgRfS/8j3qdcRJ+q6KBiuNVhsmo4H0nvSLSJmh1pqx1qu1Z3a+Bk2VNAYn8ZFjlsc6Pq2GIP5nOxeA6xwrVNYscNsZo4kINrOvrIH3dhT33fP5s0//oP7NkqbBjBu7HF6xXf07nH/SSJnq6UtuCw/KSIoQ350EKaL/6dM9HUGKKx8lPTbU8y9Qb4xGz+6zzl4ozdNvxoaZLUdqabc7APhwZylPt56FlUCianaphFtUouOtp48G4MFP92O12fvi6fzX4W9g56uAARY+5LFOD0C73cFHu0oB+P40merpU0nTINDZoFBGUoT4ziRIEf2ruaajDHPEJH13r/JRXFylyJv/q5q7BUURP38J0aEBtLTb2VNiAeCF9UeoJooDSYvU+799VL/Ej07NIjEiiKO1LbziLE8ekuzt8NGv1PaM6yF1Rpe3rD1YRW1zO/Hhgcxxa9cu+oDJDKf/GjLmeY6qCCF6RYIU0b9cUz3R6R6VEb3KR3EZfaZac8buXPn4lBswhkQyM9O1jk81u4vr2V5UR4DJQPIF/wcYVLJtleo4GxJo4s6zxwLw6JcHabTauvtKg9+Gp6AiF0Ji4ew/dvuW97erhNmFU5Iwm+R/BX3u1Dvhxg/1kmUhRO/J/5n605534ZXFajRhuNLzUaZ47O4YSelFkBIY1rGCstEMs28FVL8UUIsNuhZ8u+CkJGIzJnck3H79kH6Zq2amkRkXSnVTG/9Zc6i3T+T/LKWw6l61fc7/g9DYLm9pabPzyR7VlfeiaSldjgshhD+QIKU/rf67+iv+wKe+vhPfKXcuKuhW2QPuPVJ6Md0DkH2Vep3+A4hUeRSudXw2HKphhXN04LocZx7AqT9TrztehvVPABBgMvJ/540H4F9fHOAvH+QOrfyUT38HbY2QcgpMv67bt3y5r4KmNjupMSGcnB49sPcnhBA9JEFKf2lr7uiy2lTp23vxJX0kpSNIabLaKKlvBWBUfC9GUgBOugxu/QYW/EPfNSkpkrBAEw1WGy3tdsaNCGemq3Nq+mxVegvw8V2w601ATXG4SpKfXVvAxY99y8GKIbC2z6FVsPstMBjhwoc8+tK4c1U3LZqafOwlCYQQwockSOkv5btVh1RQvSqGI4cdKvaqbbd2+AVVKh8lLiyQmN62BjcYYORJqk25k9lk5OSMjnbu1+VkeP7infdLfWqId26Fg19gNBr4fxdN5ukfnkJMaAB7Sy1c+OhaXtpwpM860hbXtfDx7tITv56tDXLfg1bL8d/30VK1PXMJJE3t9m31Le18lacC54ukgZsQwo9JkNJfSrZ1bDdV+e4+fKna1Q4/VLW2d9LzUY7XDr8XXHkpoYEmLp7eKcfCYIDz71WjMI52eO06KN4CwLmTRvDJz+dz2th4Wtsd/O6d3dz8whZqmtpO6H5qm9q47PFvufXFraw/dII5SR/8HF6/DpYvPHagsu7fquopLAHO/J3Xt32yp4w2m4NxI8KZMDLixO5NCCH6kQQp/aVke8f2cJ3ucfVHSZzk0aNDz0fpTdLscXx/WgpZ8WH84pxxRAQHdH2D0QgXP6nKQdub4KUroOqgur3IYJ6/cRa/XziRAJOBz3LLufyJb2mzOb7TvWiaxq/f2kmZRU1p5ZYeZwTkWArXw/aX1HbZTnjtWrBZu76vrgjWPKC2z/0LhER7vaSrDf5FMtUjhPBzEqT0F4+RlGEepHROmtXLj3uZNHsMabGhfPWrM7hp/ijvbzIHwlUvqkZbzdXw4iWqEgYwGg0sOW0U79x+KnFhgRyqauJjZ/XLcWmammpxenH9ET7LLdc/P1Ld9F0eSa0e/eH/qe3RZ0NgOBSsgbdvVlNp7j65G9qbIX0uTL3a6yUrG6x8c1CN7MlaPUIIfydBSn9oa4KqvI7Ph+t0Tzft8KF/pnt6LCgCrn0TYkdBXSG8crUKBpxOSoniujmqMuh/3x4+9rU0Dfa+D/+eCQ+Oh6Yq9pVZ+MuHKg9naqrqC3O4uvm73eump1WgFxIDlz4NV78ExgDIfRdW/ga73cF7O0rY9NnrsPd9NIMJ+/ceOOaKux/uLMGhwbS0aDLi+i5IFEKI/mD29Q0MSWW7QHOAKVA1HmuqVL/QhtvQumthwZEdQYrdoXHImTg7pg+ne3olPAGueweeOh1Kt8OmZyDnVv3wNbPS+feXB9l8pJbdxfWclBLV9RpFm+DT30PRen2X9fAG7vw0nDabgzPGJ3DL/NEsfnr9dxtJaSiDL/+mts/+I4TFqamqS/8Db/4INj3NxkozS/fN5pPA34MRnmk/n/seLWRERDkjo4JJig4hKTJYbUeFMDIqmHe2d0z1CCGEv5MgpT+4pnrS50DBarBbwdowvDpPttSC5ajaTuxoh3+0tpk2m4Mgs5Hk6BAf3RwqkfecP8IHv4Cv/gaTL4aIkYDKUfnelCTe31HCC+uOcP/l2R3n1RyCz/+kRjMAzCEQFg/1RXy5di37y+cRHx7EP66Yqq+0fLS2hXa7g4DedHX99PfQ1qAWVjz5+o79J12qgt6Vv2bO4SdYHrCaTGM5lcTwb8dl2B0aJfWtqsS7sK7bSxsNcGF2Us/vRQghfESClP7gSprNOBWOblaJmk2VwytIcU31RKV7JHG6pnqy4sMwGX08snTy9bD1BSjZqlYLvuxp/dD1czJ4f0cJ724v5u4FE4gOCVBdXL9+SFUIYYBp18KZv4Uty2HN36kt2gvM4+GrphIfHoSmaYQEmGhpt3O0toWs+B5OrxSsgV1vqK+x8MGuvU5m38La7bnMK13OHFMuAAmX/YOtky+jqtFKSV0LpfWtlNW3UmZppbS+lVLnvsoGK5eenEJiZHBf/AsKIUS/6lVOyu7du5k5cyYxMTEsXbq0R/0fsrOzMRgM+seSJUs8jjscDubOncuDDz7Yuzv3Z66RlOTp6q9sGH55Kd00cQPIr3AmzfoiH6Uzo0kFARhg1+tweK1+aEZGDJOSIrHaHLy+uUgFKKvvVwHK6LPh1rVw8WMQlUJtqMphGW0s5Zb5ozhtbAIABoOBjLhQAA73dMrH3g4fOhcGnPlj9TPUSXFdCz8qOp9XbWeoHaPOhJMuw2Q0MCIymOnpMSyYksSP5mXx2wUTeXTxdN68bS7f3HUWeX+9gPsuy+5yTSGE8Ec9DlKsViuLFi1ixowZbN68mdzcXJYvX37Mc5qbm8nPz6eiooLa2lpqa2t59NFHPd7z5JNPUl9fz5133vmdHsDvWBtVrwqA5GkQnqi23Sp8WtvtfH2gUp8OGJLKnO3wR3pJmvVVPkpnKSfDKTeq7Q9/pYIEVIBx/VwVfDR8/aQKUEB1ur3ubf252mwO7tuoEm/Hmcr0dvsumc7k1MNVPQxS1j+ukq5D4+Gs33f7ln9+tp82m8aKtF+j3fgxXPNaj/OdpORYCDGY9DhIWblyJfX19Tz00EOMHj2aZcuW8eyzzx7znG3btpGdnU1CQgLR0dFER0cTEtKRh1BSUsJvf/tbHn30UQICuultMRiV7QQ0iExRAUqY+quaJtV1VtM0bvrfZq57diMf7Czx3X32N28jKXqQ4keVJWf9AULjoHKvvr4PwEVTU7gseDO/aHNOA51xN8y6ST+uaRr3rNjNhyXqWWK0OgJtnq31M+LVSMqRnlT41BfDKmcwdO6fVVVPJwfKG3hrq8r1Wfq9SRgy5oA5qMePKoQQg0mPg5QdO3aQk5NDaKj6n252dja5ubnHPGfjxo0cPXpUD1Juu+02rNaORlQ///nPycjIoKioiG+//faY17JarVgsFo8Pv+Sa6kmapl47Tfe8u72Yrw+o7b2lQ2CtmO64t8PvUn7s6pHiJyMpoFYJPudPanvVfSpYAEKKv+F+w6MYDRpfhF8Ip//G47Tl3x7m1U1FNBlCsQY7g9Hqgx7vyXKNpPRkumftwyp/KS0Hpi7u9i0PfJKHQ4PzJ4/g5PSuQYwQQgwlPQ5SLBYLWVlZ+ucGgwGTyURtba3Xc/Ly8pg3bx5r167lk08+4bPPPuPhhx8GYN26dbzxxhukpqaSn5/P9ddfzx133OH1Wvfeey9RUVH6R1paWk9vfWC5kmZduQT6SEoltU1t/OWDvfpbS+tbBvbeBkrNIbC1qMqX2I7majVNbXq7+VH+NJICKgk2dZYKEj79HZTugFeuway1s9I+k5uqrtZLpwG+PlDJXz5QQfrd35tA0AjnNE+VZ5Di6kVy3JEUTYN9H6rt0/6v24UBtxbW8mluOUYDLD1/fJfjQggx1PQ4SDGbzQQFeQ4rBwcH09zs/X++Tz75JK+88grjx49n9uzZ3HPPPbz5plqF9umnn2b27Nl88MEH/PnPf+bLL7/k8ccfJy8vr9tr3X333dTX1+sfRUVFPb31gaUnzU5Tr25Byn0r91HT1IbZWdVS6lwJeMjR2+FP9GiHf8g51ZMSHUJooJ8VlhmNKonWYIQ978Dzi1QJcMY83s36Ew6MvLD+CKAWSPzJS1txaHDpySncdNooiButrtNpJCXTOd1TVNOMzX6MNvulO6ChBALCIGt+l8OapnH/SrWq9uUzUhmTKGvuCCGGvh4HKbGxsVRWerZ3b2hoIDCw56vYJiYmUlyshtKPHj3KggUL9ES+tLQ0EhISyM/P7/bcoKAgIiMjPT78Tqul45eUPt2jghRLVSmvbVaBlSu5clCPpLx7Ozx6ilpE0M19K/fx/DsfqE+8JM363SiKS1I2zHTmnLTWw4gpsPhlrpk3DoA3txylrL6VJc9vwtJq4+T0aJZdMkX9DMePVedVH/C45IiIYILMRmwOjeK6Y3y/81aq19FnQkDX8uDV+yvZUFBDoNnIz88Zd8KPKoQQg0GPg5SZM2eybt06/fOCggKsViuxsbFez5kzZ47HiMe6devIyFAVE6mpqbS0dPxPu7GxkZqaGlJSUrpcZyC12RxsLKjh9U3fYaTGlTQblaa6moKek1JTqYKzxbPSWTRVNdIqq2/FMRgrfFotsOMV9Qv5hUtUd1RUN9mX1h8h2XoIAGvcRI/T/DIfpbOzfqfyaEZMgR+8CcFRnDYmnqz4MBpabSz691ryK5tIigrmyetmEBzgHCmKcwYpnaZ7jEZDR4XPsaZ89juDlPHf63LI4dD4+8dqhPH6ORm+bYInhBADqMdByvz587FYLDz33HMALFu2jHPOOQeTyURdXR12u73LOZMnT+aWW25hw4YNPP/88zz44IPcdtttACxevJinn36aL774giNHjnD77bczYcIEsrN928OhrrmNK59ax2/e3kmj1Xb8E9zpSbNTO/Y5R1Ii7HXEhwdy1wUTGBEZjMEA7XaN6qa2bi7k54q3qLb/AHVH4MXLobWevLIGGqw2JhoLAXgqL9Sjl46++rE/9EjxJjgKblkDt36td6A1Gg1cl6OC68oGK8EBRp7+4SkkRriNeMSNUa81+eDwnNZx9Urx2h6/vlhN92CAsed3OfzhrlJySy1EBJm5/YwxJ/Z8QggxiPQqJ+WZZ57hjjvuID4+nhUrVnD//apcMiYmhl27dnU55x//+AdBQUGceeaZ/PGPf+SBBx7g+utVi+9zzz2X+++/n9tuu40JEyZw4MAB3nzzTZ/3cUiMDCY5KhhNg11H63t3cuekWeCI1VmeSiP3LBhHVGgAASYjCeEqv2dQTvkUbVSvGadCWCKU74JXr2XroVIiaSLVoKqXntkfwqtuI1J+WX7cHaOpS9+Ry09JJTxI5dE8eMW0ruv5xGSA0axWIm7wLC3PdHaaLfDWK2X/x+o1dWbHCJyTpmk8sUpNqS05bRQxYT2fXhVCiMGuV9mLF110Efn5+WzZsoWcnBzi4uIAvHaejY6O5p133vF6vR//+Mf8+Mc/7s0tDIipadGU1JexvaiOOaPjen5ip6RZTdP43SfF/E8zYDRoLBrb8Zd3UnQIFQ1WSupayU7tw5sfCEUb1OvkSyBtFjy3EA5/zeSqXzHZeBoADUEjsbSG88f39pCdGsWYxHAKa9R0h88WFjwBkcEBvHZLDpYWW/c/E6YAiMlSU2BVByCq45vaMZLiZbrHFaSMv6DLoW/zq8kttRASYNKbywkhxHDRq7b4ACNHjmThwoV6gDIUTUuLBmBHUV3PT2qtV0P9AElqJOXd7cWsza+jFlWJYXDrOpscpQKWssE2kuJwwNFNajttlpraWvwymimQ6Y1r+Lv5PwCEp0/l7AmJtNkc/OSlrewutuDQICLITELE4Gw+Njk56thBq2vKpze9Utqa4NBqtT1+QZfD/1mj8nuumplGdKiMogghhpdeBynDgStI2d6bIKV0h3qNSoewOOwOjQc/dbbHdytDdhnpDFIGXRly5T6wWlSpbKKzm2zWfKrPewyHZiDNqJ7RMPIkHrxyKinRIRyubuanL28FYFRiuM+n9PpNfPdBSoZzuqfbMuT8r9Qq2dEZkDDB41BeWQOr91diNMCPTs1CCCGGGwlSunFSShRGA5RZ1EqyPdJpqufzveUcrW0hOjSA6IRkdcxtkcHkKFWhUTLYghTXVE/qDDB1zBauCZjLPbYbOt43YjLRoYE8es10zEaD/px+n49yIvQKH88y5KTIYALNRtrtWteg1L2qp1Pw9vTXahTleyclke6cMhJCiOFEgpRuhAWZGTdCTdH0eDRFT5qdBsDybw4DcPXMdEzhXUdSkqIH6XSPK2k2bbbH7k2Ha3nRfi6fZPwKJl4E41R+xcnpMdz1vY4RAr8uPz5RXqZ7jEYD6bHdrIbscMD+T9R2p9LjcksrK7arsvUlp8koihBieJIgxYteT/noIynT2VtqYd2hakxGA9fNyeh2uifJOd1TUjdIR1I6BSmbD9cAYJh1E1z1AgR2jJj8eF4WF01NxmiA08bGD9itDjhXQ7e6Qmj3/L522yuleIv6mQiKhPS5Hu9/7pvDtNs1ZmXGMl3W6BFCDFMSpHjRq+TZllqoLVDbSdN4/tvDgFoELiU6xEuQoqZ7yi2t2AdLQ7emqo7k4NRT9N21TW0ccPZAOSWza3M/g8HAI1dPY+f/O5/s1OiBuFPfCEuAoChAU+sXucl0Ttccdi9DzvtIvY45B8wdSbGNVhsvbVAt+G+a37H2kRBCDDcSpHgxLT0agJ1H644fRLiSZqMzqNHCeWebGqa/0ZXs2GklZIDEiCCMBrA5NKobrQwKrqmehAkQ0vHX/eYjapHJMYnhxHrp42EwGPQ+I0OWweB1DR9X8qxHQze99Nhzque1TUU0tNoYFR/G2RMS++12hRDC30mQ4sXYxAhCA000tdk56Bwl8MptqufVTYVYbQ4mJ0dySobzF3k3Iylmk1HvWDpokmf1qZ5ZHrtdUz0zM2VawtsaPlmdp3tqD0NFLhhMaiTFyWZ38N+1alRuyWmjMBqHaCWUEEL0gAQpXpiMBqY4u4oed8rHmTRrHzmVF9apYfobT83qKLUNd/417BakQEfybOmxFp7zJ16SZjc6g5RTMryv4zRseFnDx9XQrbC6WY3M5TlHUdLnQGjHv9tHu8sormshLiyQS0/27TpWQgjhaxKkHINrymfbsYKUgjV6bsHG9lGU1rcSHx6oLyIIdDvdAx1lyIOiV4qtDUpUrxP3IKWlzc7uYrV8wKwsCVK8TfckR4cQYDLQZneopRD00uOOLrOapvGfNSrn54dzMjsWLxRCiGFKgpRjmOZM8vQ6klK6A165BuxtMPEiHsxT0zrXzEonyOz2C8Y13dPepDqMOnU0dBsEIyllu8DWqnJR4joWudtxtI52u8aIyCBSY2R1Xm/TPSajgTRnGfLR0nI4/I064NZldv2hGnYXWwgyG1VVmBBCDHMSpByDayQlr7yBlrZOqzzXFKjVf9saIPM0ds/+B5sL6zAbDfwgp9MvmMBwMDvX7emuDHkwjKS4lx67NR3bVOCc6smMHbqdZHsj1jmS0lILTdUeh1x5Ke37PwNHu5oaco28AM+uVRVBl89I9ZqALIQQw4kEKceQFBXCiMgg7A6NXcVuKyI3VsALl0BTBYyYAle/xH83lgKwMDuJxMhgzwsZDG7Js25dZ6PVyEOPu9r6kpek2U3Oyp5Z3ZQeD0uBoRDpXFywc4WPM0iJO9K1qudIdRNf7KsA4EfzpHmbEEKABCnHNbXzlI+1AV66XPVFic6AH7xJZXswH+xQQcqN3tZY0fNSulm/x98TZzWt2yZudofGVmeQcopU9nTQ1/DxnPLJjA8liWrG136ldpx0mX7s+W+PoGlwxviEod2VVwghekGClONwTflsL6oDmxVevVblooTGw3XvQMRIXt5QSJvdwbS0aL0JXBfdlCG7EmfLG6z+3dCt/ig0lKpy2eST9d17Sy00Wm2EB5mZMDLShzfoZ7ys4ZMRF8aN5o8x4YDM0/QlFBqtNt7YXATADXMzB/BGhRDCv0mQchyu5NntRXXw3k+hYLXKMfnBmxA3mvqWdpZ/q/pa3HhqpvcLdROkJEQEYTIasDs0Khv8uKGbaxQlKVtNZzi5+qOcnBGDSfp5dPCyhs+ocDuLTV8C4JjzU33/W1uO0mC1MSohjPljEwbsNoUQwt9JkHIcU1KjMBhgnOVb2PkaGM1qbZrk6QD8+8sD1Da3MzYxnIVTkrxfqJsyZJPRwIiIIABK/LnCx9uigno+ikz1eIjvPkhJPvQaEYYW9jtSKEucB4DDobHcuYzCDXMzpXmbEEK4kSDlOCKCA5icEMCfzMvVjpzbYfRZgFqHxfUL5ncLJ2I2HeOfM8xbQzdnrxR/Xmiwm6RZTdM8KnuEG9d0T80hcDirwmxtmDY8CcDT9oUcrlFB6eoDlRRUNRERZOayk1N9cbdCCOG3JEjpgZ8FfUi6sZKGwEQ4/Tf6/ntX7qXdrnH6uATOGH+cNVa6me6BjjJkv+2V0takeqSAx0hKUU0LFQ1WAkwG73k4w1VUKpiCVP+cOtWBmD1vQ0MJdaZYVthP5YizPf5z3xwG4MqZaYQN9bWNhBCilyRIOZ7qfM6qfhmA58JvhiBVebEuv5pP9pRjMhr4/cKJx7+Oa7qn0VuQ4qcjKcVbQbNDZIr65eu0yZmPMiUlSjqjdmY0uXWezVfVUd8+CsDmEVfSRgCHq5s4WNHImv2VGAxw/ZxM392vEEL4KQlSjkXTYOVvMDnaWGOfwtPVU3A4NOwOjb98kAuo7rJjR0Qc/1peR1JcrfH9dCTFW38UfVFBmerplit5tuoAHPoKyndDQBgV4xYDaqrweedU4dkTRpAeF+rlQkIIMXzJ+PKx7PsADn6GZgxgmf1GGqx2DlU1sfVILbmlFiKCzfzi3HE9u5YrSGmuAocDjCo+TI7285GUbpJmNU1jg+SjHFucW6+Ug5+p7ZOvIykpGShmT4mFr51J1D86VlWYEEIMYxKkeNPWBCvvAsBw6p1EHJgIh2v5Nr+KR79UVRt3njW25+3LQ+PUq+ZQLdPD1Ocjo/w4cdZSAoXr1LbbSMrWwloKqpoIDjAye5QEKd1yreFz4DOoLwKDEXJuI8uuus4erVUjZ+NHRDBndJyv7lIIIfyaTPd4s+YBsByFqHQ47Vd659m/f5xHZYOVjLhQfji3F4vAmQMhWF3Ds6GbGkmpaGjFZnf00c33gZZaeOFSsFogYQKMnKofeml9IQAXZicTGRzgqzv0b64Kn3rVpI1J34eYTFJiQjx6ytxwaqaseSSEEF5IkNKdyv3w7b/V9vfug8BQvfNso9UGwN3fm+i50nFPdJOXEh8ehNlowKFBhb80dGtvgZevhsq9EJEE174BJjXoVtfcxge71BIA18xO9+Vd+je3hQMBmKuatwWYjPpq0dGhAVw8LWWg70wIIQYNCVI60zT46Fdqldqx58P4BQAeZbazs2I5f/KI3l+7myDFaDQwItKPypDtNnjzR1C0HoKj4AdvQXRHMPLW1mLabA4mjIxgupQeexca2zHFl3EqpMzQD7nW5rl6ZjohgVIZJYQQ3kiQ0lnpDjj8tepz8b371ArGQEp0CKPiwwgwGfjDhZO+2xB9eNeVkKEjebbE13kpmgYf/AzyPgJzMCx+FUZMdjus8fIG1ffj2tnpMk1xPGk56nXeLz12Lz1/PD85czR3nDXGBzclhBCDhyTOdpY8DW76CipyIXaUvttgMPDqzTk0Wm2M+q6r1B6zDLmWMl9X+Hz5F9j2okryvPy/kDHX4/DGghryK5sIDTRx8XSZpjiuix8HS7FHoAcwMSmSiUmyIKMQQhyPBCndSZ6mr1DrLjEymOP0lT2243Sd9en6PRuegq8fVNsX/hMmLOzylpc3qoTZi6YmEyEJs8cXEq0+hBBCfCcy3TOQ9EUGvXSd9dV0T0M5fHy32j7r9zDj+i5vqWlqY+WuMkASZoUQQgwMCVIGkpeRFL1XisVHQUrBGtX6fsQUOO1X3b7lzS1FtNkdnJQSSbazHFsIIYToTxKkDCQvQYredbbOR9M9BavU6+gz9ERhd5qm8cpG1e/jmlm96A0jhBBCnAAJUgZSWPfVPa71eyobrbTZfNDQrWCNes06vdvD6/KrKahqIizQxEXTkgfwxoQQQgxnEqQMJFdOitUC7R1TO3FhgQSYDGia6jw7oGoPQ10hGM2QPqfbt7zkTJi9eHoK4UGSay2EEGJgSJAykIKjweisimnuGE0xGg2MjPLRQoOHVqvXlFMgqGtpdVWjlU/3SMKsEEKIgSdBykAyGI7TKwVKBjovRZ/qmd/t4Tc2H6XdrjE1LZrJyVEDeGNCCCGGOwlSBppehtw5L0WNpAxoQzdN6whSRnXNR1EJs2qq59pZMooihBBiYEmQMtCOM5IyoNM9lXnQVKFa4KfO7HJ4e1EdhTXNhAaauHBq0sDdlxBCCIEEKQPPFaQ0Vnjs7li/ZwCnewqc+SjpOWAO6nL4w51qteNzJo4gNFASZoUQQgwsCVIGmpeusyOdKyGXDWRDt2OUHjscGh/tUkHKwmwZRRFCCDHwJEgZaF56pSRHuxJnByhIcdjVas/QbZCyraiOkvpWwgJNnD4uYWDuSQghhHAjQcpAO84ig1WNVqw2e//fR+kOaK2HoEhImtrlsGuq59xJIwgOMPX//QghhBCdSJAy0LwEKbFhgQSa1bejwmLt//twTfVkzgOTZ76J51SPdJgVQgjhGxKkDDQvJcgGg0EfTRmQ5Nlj9EfZWlhLmaWViCAzp42N7/97EUIIIbohQcpAC09Ur02Vqk+Jm6SB6jpra4PCdWq7myDlA5nqEUII4QckSBlooc6RCUe7yglxo3edre/nkZTizdDerO4lcZLHIYdDY+VuqeoRQgjhexKkDLSAYJWsCt1U+AzQdI/7VI/B4HFoS2Et5RYrEcFm5slUjxBCCB+SIMUXvPRKSY8NBaCwpp+DFNeigt1M9biqes6bNJIgs0z1CCGE8B0JUnxBr/Dx7DqbFqOClKM1zf33tdua4Ogmtd1pvR67W1XPhTLVI4QQwsckSPEFL63x05wjKUdrW3A4tM5n9Y3C9SofJioNYrI8Dm0+XENFg5XIYDOnjpGpHiGEEL4lQYovRDtXFK497LE7KSoYk9FAm91BeUM/VfgUuE31dMpH+dA5inL+5JF6zxYhhBDCV+Q3kS/EjVav1Qc9dptNRj15tqi/8lK89EdRUz1lgFT1CCGE8A8SpPhC3Fj1WnWgy6GO5Nl+yEtpqVPt8KFLkLKxoIaqRitRIQEy1SOEEMIvSJDiC/HOIKX2sGqs5saVPFvUH0FK2S7QHBCdAZGe7e4/3FUCwAWTRxJgkh8LIYQQvie/jXwhIgkCwkCzQ90Rj0Ou5Nl+CVKqnSM3CeM9dtsdGh/vlqkeIYQQ/kWCFF8wGDryUjpN+ehBSm1/BCn56tU13eS0v7yBqsY2woPMzBkd1/dfVwghhPgOeh2k7N69m5kzZxITE8PSpUvRtOOXymZnZ2MwGPSPJUuWdHlPXV0dSUlJHD58uLe3NDi5pnyqOwUpMao1fr8kzroCIleA5LSrWLXnn5wcKVM9Qggh/EavfiNZrVYWLVrEjBkz2Lx5M7m5uSxfvvyY5zQ3N5Ofn09FRQW1tbXU1tby6KOPdnnf0qVLKSsr69XND2pxY9RrpwofV+JsmaWV1nZ7335NV0AU7zmSstsZpExJierbryeEEEKcgF4FKStXrqS+vp6HHnqI0aNHs2zZMp599tljnrNt2zays7NJSEggOjqa6OhoQkJCPN6zZs0a3nvvPeLihtFUg17h4xmkxIYFEhqo2tEX9+UaPrY2qD3i+bWdXCMpU1IlSBFCCOE/ehWk7Nixg5ycHEJD1V/72dnZ5ObmHvOcjRs3cvToUT1Iue2227Barfpxq9XKLbfcwr/+9S/Cw8O9XsdqtWKxWDw+BrV410iK53SPwWDonzLk2sMqUTcwHCJG6rttdgd7S9W/5UkykiKEEMKP9CpIsVgsZGV1tFI3GAyYTCZqa2u9npOXl8e8efNYu3Ytn3zyCZ999hkPP/ywfnzZsmWMGzeOq6666phf+9577yUqKkr/SEtL682t+59YZ15IU6XqX+ImtT/W8Kl2y0dx6zR7sLKR1nYH4UFmsuLC+u7rCSGEECeoV0GK2WwmKCjIY19wcDDNzd5/mT755JO88sorjB8/ntmzZ3PPPffw5ptvArB3716efPJJnnjiieN+7bvvvpv6+nr9o6ioqDe37n+CIyHcOaLhqrpxStcrfPpwuseV+9J5queomuqZlByJ0WjofJYQQgjhM70KUmJjY6msrPTY19DQQGBgYI+vkZiYSHFxMZqmcfPNN/PXv/6V5OTk454XFBREZGSkx8eg563CJ1bl7BRW9+FISpUkzQohhBhcehWkzJw5k3Xr1umfFxQUYLVaiY2N9XrOnDlzPEY91q1bR0ZGBoWFhaxdu5alS5fqCbWFhYVkZ2fz8ssvf4dHGYS89UqJ6YdeKfpIyhiP3bskSBFCCOGnehWkzJ8/H4vFwnPPPQeofJJzzjkHk8lEXV0ddnvXktnJkydzyy23sGHDBp5//nkefPBBbrvtNlJSUigoKGD79u36R3JyMh999BEXXXRR3zydv3NNvXQuQ47rh8RZvUdKR5BiszvIlaRZIYQQfsrcqzebzTzzzDMsXryYpUuXYjQaWbVqFQAxMTFs27aNadOmeZzzj3/8gxtvvJEzzzyTxMREHnjgAa6//noAMjMzu1w/NTX1mFU+Q0p890FKqrOhW0OrjfrmdqJCA07s67TUQnOV2nYLUvIrm2htdxAWaGJUvCTNCiGE8C+9ClIALrroIvLz89myZQs5OTl6bxNvnWejo6N55513enTtYdNt1kVv6JYPDgcY1cBWaKCZ+PAgqhqtFNU2ExV6gqMcrsTciCQI6ggAOzrNRknSrBBCCL/znXqgjxw5koULFw6v5mv9IToDjAFgawFLscchPXm2L6Z8upnqgY6kWZnqEUII4Y9koRZfMpkh1tl3pssaPn24GrJrOineW6fZIVApJYQQYsiRIMXXvLTH79Ous9XdJ83uKZHKHiGEEP5LghRfc5Uhd0qedU339ElDt6qujdxcSbOhgSay4odJorIQQohBRYIUX/Pa0K2PWuM7HFDjTJyN7xhJ6UiajcQkSbNCCCH8kAQpvuZluseVk3K0tgWHo/vKqR6xHAVbq0rQjUrXd3d0mo3+7tcWQggh+pEEKb7myhOpL4L2jqmdpKhgzEYDbXYH5Q2t3/36rsqe2FEqUddJkmaFEEL4OwlSfC0sHoKjAA1qDum7zSYjydF9sIaPq0eKW2WP3aGRW6I6zUrSrBBCCH8lQYqvGQxuUz7dLzR4Qsmz3VT25Fc20tJul6RZIYQQfk2CFH+gd57tvgy5t71Simqamfm3z/nNmzvRumnktuuoJM0KIYTwfxKk+IP47oOU1O/Y0O3ZtQVUNlh5bXMRzaV5zq/RMd2zSzrNCiGEGAQkSPEHXqZ79JGU2p4HKc1tNt7aehSAINoIaS71/Bq4V/ZIkCKEEMJ/SZDiD/TpngPgtlBj2vG6zhZvgX+Mh68f0ne9t72EhlYbGXGhXJjcjNGg0WgIxx4cA6ik2T2SNCuEEGIQkCDFH8SNBgzQWg/N1frutBiVOFtusdLabvc8x26D9+6ExjJY8wA016BpGi+sPwLAtbPTuXt2AAD77SP5z9cFABxyS5odlSBJs0IIIfyXBCn+ICAEotLUttuUT2xYIGGBJgCK6zpV+Gx6Bsp3q+32Ztj8X7YX1bGnxEKg2cgVM9KIt6qA5ZCWzEOf5bGnpF7PR5mUJEmzQggh/JsEKf5CX8OnI0gxGAzdT/k0lMFXf1Pbo89Wrxue4pVvVeLthdlJxIQF6l1szQljaLdr/OK17Ww+UgtI0qwQQgj/J0GKv9DX8Om80GA3a/h8+gewWiBlBix+BSKSoakC0543ALguJ8PjWmfPO5X48CD2lzfyysZCQPJRhBBC+D8JUvzFcdbw0UdSCr6GXa8DBljwDzAHQc5tAPzI8AEnJYUzLS1aJeA6R2UiUibxwOXZQEde7pRUCVKEEEL4NwlS/EU30z0A6a6uszUtYG+Hj36lDpzyI0g5GQDHydfTSChjjcUsHV2EwWCApiqViIsBYrM4c0Ii185WCwyGBJgYLUmzQggh/Jz5+G8RA8I13VNToCp3nIsBprn3Sln/BFTug9B4OPsP+qlri9rYazuTW8wfMq/yFeAHHcFOdJpKzAV+t3AiVpuD7NQoSZoVQgjh9yRI8ReRqWAOAVsL1B3RR1ZcQYq1pghW3afee+6fISRGP/WF9UfYbbuAJeaPMR1ZCyXbOnJb3Jq4hQaa+ccVUwfmeYQQQogTJNM9/sJodJvy6chLceWk/Ny+HNqbIG02TF2sHy+pa+GLveWUEkfTuO+rnd8+2lHK7NYOXwghhBhMZCTFn8SNVr1P3rhBJcQCIcCO4HaiaEQzGDEsfFAFNE6vbCzEocGcUXFEnvkL2P827HkXRkx2XnNMly8jhBBCDAYykuJPRp2pXtuboaVW/4iiEYBDY2+EkVP0t9c2tfHqpiIAfpCTAUnZMOoM0OxQtlO9SYIUIYQQg5SMpPiTU26EMeeoIMXNso/28vG+Gia0TyH5vT3sL2/gQEUjlQ1WABIigjhv8gj15rk/hUOrOk6W6R4hhBCDlAQp/iY6rcuuoCQo3HuQwr0VXY6lRIew9PzxBJicg2Kjz4bESVCRqxJxI5L7+46FEEKIfiFByiBw8fQU1h+qJjzIzLgREYxJDGes8zU8qNO30GCAuXfCu7eqvBSjzOgJIYQYnCRIGQRGJ4Tzxq1ze37C1KtVsJI0rd/uSQghhOhvEqQMRQaDClSEEEKIQUzmAoQQQgjhlyRIEUIIIYRfkiBFCCGEEH5JghQhhBBC+CUJUoQQQgjhlyRIEUIIIYRfkiBFCCGEEH5JghQhhBBC+CUJUoQQQgjhlyRIEUIIIYRfkiBFCCGEEH5JghQhhBBC+CUJUoQQQgjhlwbtKsiapgFgsVh8fCdCCCGE6CnX723X7/FjGbRBSkNDAwBpaWk+vhMhhBBC9FZDQwNRUVHHfI9B60ko44ccDgclJSVERERgMBj69NoWi4W0tDSKioqIjIzs02v7K3lmeeahSp5ZnnmoGqzPrGkaDQ0NJCcnYzQeO+tk0I6kGI1GUlNT+/VrREZGDqpvfF+QZx4e5JmHB3nm4WEwPvPxRlBcJHFWCCGEEH5JghQhhBBC+CUJUroRFBTEH//4R4KCgnx9KwNGnnl4kGceHuSZh4fh8MyDNnFWCCGEEEObjKQIIYQQwi9JkCKEEEIIvyRBihBCCCH8kgQpQgxhdXV1bNiwgdraWl/fihBC9JoEKZ3s3r2bmTNnEhMTw9KlS3u0tsBgVFVVRVZWFocPH9b3DeVnX7FiBaNGjcJsNjNt2jT27t0LDO1nfuONN8jMzGTJkiWkpqbyxhtvAEP7md1dcMEFLF++HIDVq1czceJE4uPjeeihh3x7Y33szjvvxGAw6B9jxowBhsf3+Te/+Q2LFi3SPx+qz7x8+XKP77HrY/ny5UP6ZxskSPFgtVpZtGgRM2bMYPPmzeTm5ur/kxtKqqqquPDCCz0ClKH87Pn5+dx4443cd999FBcXM27cOJYsWTKkn7m+vp7bb7+dNWvWsGvXLh577DGWLl06pJ/Z3UsvvcQnn3wCQGVlJRdddBGLFy9m3bp1vPTSS3z11Vc+vsO+s3nzZj788ENqa2upra1l27Ztw+L7vHPnTh5//HEeeeQRYGj/P+yaa67Rv7+1tbUUFRURHx/PxIkTh/TPNgCa0L3zzjtaTEyM1tTUpGmapm3fvl079dRTfXxXfe/ss8/WHnnkEQ3QCgoKNE0b2s/+/vvva0899ZT++ZdffqmFhIQM6WcuLCzUXnzxRf3zHTt2aOHh4UP6mV2qq6u1ESNGaOPHj9eee+457eGHH9YmTJigORwOTdM07d1339WuvfZaH99l32hvb9ciIyO1hoYGj/1D/ftst9u12bNna3/4wx/0fUP9md397W9/02666aYh/bPtIiMpbnbs2EFOTg6hoaEAZGdnk5ub6+O76ntPP/00d955p8e+ofzsF154ITfffLP+eV5eHmPHjh3Sz5yWlsa1114LQHt7Ow8//DCXXHLJkH5ml//7v//jkksuIScnB1A/22eeeaa+EOmsWbPYsmWLL2+xz+zatQuHw8G0adMICQnhggsuoLCwcMh/n5988kl27dpFZmYm7733Hm1tbUP+mV1aW1t55JFH+O1vfzukf7ZdJEhxY7FYyMrK0j83GAyYTKYhl3To/owuw+XZ29raePDBB7n11luHxTPv2LGDkSNH8vHHH/Ovf/1ryD/zV199xRdffMHf//53fV/nZ46MjKSkpMQXt9fncnNzGT9+PC+88AI7d+7EbDZz8803D+nvc2NjI3/84x8ZNWoUR44c4eGHH2bevHlD+pndvfzyy8yePZvMzMwh/bPtIkGKG7PZ3KW9cHBwMM3NzT66o4EzXJ79j3/8I2FhYSxZsmRYPHN2djaffvopY8eOHfLP3Nrayi233MITTzxBRESEvr/zMw+V5wW49tpr2bx5M3PmzGHs2LE8/vjjfPbZZzgcjiH7fX777bdpamriq6++4k9/+hOfffYZDQ0N/Pe//x2yz+zuySef5NZbbwWG9s+2iwQpbmJjY6msrPTY19DQQGBgoI/uaOAMh2f/8ssveeyxx3j55ZcJCAgYFs9sMBiYMWMGzz//PG+//faQfua//OUvzJw5k4ULF3rs7/zMQ+V5u5OYmIjD4WDkyJFD9vt89OhRcnJyiI+PB9Qv6uzsbOrq6obsM7scPHiQgwcPcu655wLD42dbghQ3M2fOZN26dfrnBQUFWK1WYmNjfXhXA2OoP3tBQQGLFy/mscceY9KkScDQfubVq1ezdOlS/fPAwEAMBgMTJ04css/88ssvs2LFCqKjo4mOjubll1/m9ttv5/nnn/d45m3btpGSkuLDO+07S5cu5eWXX9Y/X7duHUajkSlTpgzZ73NqaiotLS0e+44cOcI///nPIfvMLq+//joXXnghAQEBQNf/hw2ln22drzN3/Ul7e7uWkJCg/fe//9U0TdOWLFmiXXjhhT6+q/6DW3XPUH725uZmbdKkSdpNN92kNTQ06B9tbW1D9plLSkq0yMhI7amnntIKCwu1H/7wh9oFF1wwpL/PRUVFWkFBgf5x2WWXaQ888IBWWVmpBQcHa5999pnW1tamXXDBBdodd9zh69vtEy+88IKWlZWlff7559onn3yijRs3TrvhhhuG9Pe5qqpKi4yM1J544gmtqKhIe+SRR7Tg4GCtsLBwyD6zy2mnnaY9++yz+udD+WfbRYKUTlasWKGFhoZqcXFxWkJCgrZnzx5f31K/cQ9SNG3oPvu7776rAV0+CgoKhuwza5qmffrpp9qkSZO0iIgI7fLLL9cqKio0TRu63+fOrr/+eu25557TNE3TnnjiCS0gIECLiYnRsrKytLKyMt/eXB+66667tKioKC02Nla78847tcbGRk3Thvb3ee3atVpOTo4WEhKijRo1Snvvvfc0TRvaz9zc3KwFBgZqe/fu9dg/lH+2NU3TDJo2RFry9aGysjK2bNlCTk4OcXFxvr6dATUcn12eeXg8c0FBAfv27eO0004jPDzc17czIIbj93k4PvNQ/tmWIEUIIYQQfkkSZ4UQQgjhlyRIEUIIIYRfkiBFCCGEEH5JghQhhBBC+CUJUoQQQgjhlyRIEUIIIYRfkiBFCCGEEH5JghQhhBBC+CUJUoQQQgjhl/4/Etr5UD1qCTUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "估计的斜率 (a):0.9913632735670562\n",
      "估计的截距 (b):0.006283177671575203\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avg_loss: -1.1501910535116621e-15\n",
      "日iopv与收盘价均误差 -0.0016545321277403537\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(len(data['单位净值'])),data['单位净值'])\n",
    "plt.plot(range(len(data['单位净值'])),db['收盘'][::-1])\n",
    "plt.show()\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "import numpy as np\n",
    "from scipy.optimize import curve_fit\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = np.array(data['单位净值'][::-1])\n",
    "y = np.array(db['收盘'])\n",
    "\n",
    "# 定义线性回归模型函数\n",
    "def linear_model(x, a, b):\n",
    "    return a * x + b\n",
    "\n",
    "\n",
    "# 使用最小二乘法估计参数\n",
    "params, covariance = curve_fit(linear_model, x, y)\n",
    "\n",
    "# 提取估计的模型参数\n",
    "a_est, b_est = params\n",
    "\n",
    "# 打印估计的参数\n",
    "print(f\"估计的斜率 (a):{a_est}\")\n",
    "print(f\"估计的截距 (b):{b_est}\")\n",
    "\n",
    "# 绘制原始数据和拟合线\n",
    "plt.scatter(x, y, label=\"原始数据\")\n",
    "plt.plot(x, linear_model(x, a_est, b_est), color='red', label=\"拟合线\")\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "print('avg_loss:',np.average(linear_model(x, a_est, b_est) - y))\n",
    "get_like = linear_model(data_fund['收盘价'], a_est, b_est)\n",
    "\n",
    "plt.plot(range(len(get_like)), get_like,c = 'r',label = 'iopv')\n",
    "plt.plot(range(len(get_like)),data_fund['收盘价'],c = 'b',label = '收盘价')\n",
    "plt.legend()\n",
    "plt.plot()\n",
    "print('日iopv与收盘价均误差',np.average(get_like - data_fund['收盘价']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "日iopv与收盘价均误差 -0.002115222109368607\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(len(get_like)), get_like,c = 'r',label = 'iopv')\n",
    "plt.plot(range(len(get_like)),data_fund['收盘价'],c = 'b',label = '收盘价')\n",
    "plt.legend()\n",
    "plt.plot()\n",
    "print('日iopv与收盘价均误差',np.average(get_like - data_fund['收盘价']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30.076343173112225\n"
     ]
    }
   ],
   "source": [
    "# 每只基金的折溢价 和 次数\n",
    "up_down = []\n",
    "for i,j in zip(get_like,data_fund['收盘价']):\n",
    "    cha_values = abs(i-j)\n",
    "    if cha_values > 0:\n",
    "        up_down.append(cha_values)\n",
    "print(np.sum(up_down))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.9778191857672902, 0.98]]\n",
      "['s']\n"
     ]
    }
   ],
   "source": [
    "T = 0\n",
    "db_arr = []\n",
    "tii = [None]\n",
    "for i,j in zip(get_like,data_fund['收盘价']):#i iopv ,j 收盘\n",
    "    if i < j and tii[-1] != 's': # 溢价\n",
    "        db_arr.append([i,j])\n",
    "        tii.append('s')\n",
    "    if i > j and tii[-1] != 'b': # 折价\n",
    "        db_arr.append([i,j])\n",
    "        tii.append('b')\n",
    "tii = tii[1:]\n",
    "print(db_arr)\n",
    "print(tii)\n",
    "for i,j in zip(db_arr,tii):\n",
    "    # 投资金额小于总交易10%\n",
    "    if j == 's':\n",
    "        diff = i[1] - i[0]\n",
    "        # 成本 = i[0]*手数*申赎费 + i[1]*交易费（股票/etf)\n",
    "    if j == 'b':\n",
    "        diff = i[0] - i[1]\n",
    "        # 成本 = i[0]*手数*申赎费 + i[1]*交易费（股票/etf)\n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d244557e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "345104b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Datas': [{'FSRQ': '2023-11-03', 'DWJZ': '0.4494', 'JZZZL': '0', 'LJJZ': '1.9230', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-11-02', 'DWJZ': '0.5458', 'JZZZL': '0', 'LJJZ': '1.8030', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-11-01', 'DWJZ': '0.6610', 'JZZZL': '0', 'LJJZ': '1.7920', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-10-31', 'DWJZ': '0.6301', 'JZZZL': '0', 'LJJZ': '1.7660', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-10-30', 'DWJZ': '0.4750', 'JZZZL': '0', 'LJJZ': '1.7200', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-10-29', 'DWJZ': '0.9274', 'JZZZL': '0', 'LJJZ': '1.7810', 'NAVTYPE': '0', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-10-27', 'DWJZ': '0.2193', 'JZZZL': '0', 'LJJZ': '1.9560', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-10-26', 'DWJZ': '0.5231', 'JZZZL': '0', 'LJJZ': '2.1660', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-10-25', 'DWJZ': '0.6115', 'JZZZL': '0', 'LJJZ': '2.1320', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-10-24', 'DWJZ': '0.5431', 'JZZZL': '0', 'LJJZ': '1.9100', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-10-23', 'DWJZ': '0.5913', 'JZZZL': '0', 'LJJZ': '1.8440', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-10-22', 'DWJZ': '1.2638', 'JZZZL': '0', 'LJJZ': '1.7490', 'NAVTYPE': '0', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-10-20', 'DWJZ': '0.6215', 'JZZZL': '0', 'LJJZ': '1.5190', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-10-19', 'DWJZ': '0.4576', 'JZZZL': '0', 'LJJZ': '1.4080', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-10-18', 'DWJZ': '0.1866', 'JZZZL': '0', 'LJJZ': '1.3660', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-10-17', 'DWJZ': '0.4156', 'JZZZL': '0', 'LJJZ': '1.5190', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-10-16', 'DWJZ': '0.4097', 'JZZZL': '0', 'LJJZ': '1.5950', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-10-15', 'DWJZ': '0.8213', 'JZZZL': '0', 'LJJZ': '1.6840', 'NAVTYPE': '0', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-10-13', 'DWJZ': '0.4092', 'JZZZL': '0', 'LJJZ': '1.8560', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-10-12', 'DWJZ': '0.3765', 'JZZZL': '0', 'LJJZ': '1.9420', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-10-11', 'DWJZ': '0.4807', 'JZZZL': '0', 'LJJZ': '2.0460', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-10-10', 'DWJZ': '0.5610', 'JZZZL': '0', 'LJJZ': '2.0950', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-10-09', 'DWJZ': '0.5809', 'JZZZL': '0', 'LJJZ': '2.1020', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-10-08', 'DWJZ': '5.7507', 'JZZZL': '0', 'LJJZ': '2.0990', 'NAVTYPE': '0', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-09-28', 'DWJZ': '0.7254', 'JZZZL': '0', 'LJJZ': '1.7570', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-09-27', 'DWJZ': '0.4405', 'JZZZL': '0', 'LJJZ': '1.6060', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-09-26', 'DWJZ': '0.5090', 'JZZZL': '0', 'LJJZ': '1.4920', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-09-25', 'DWJZ': '0.4085', 'JZZZL': '0', 'LJJZ': '1.3470', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-09-24', 'DWJZ': '0.8670', 'JZZZL': '0', 'LJJZ': '1.2570', 'NAVTYPE': '0', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-09-22', 'DWJZ': '0.4197', 'JZZZL': '0', 'LJJZ': '1.2340', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-09-21', 'DWJZ': '0.4358', 'JZZZL': '0', 'LJJZ': '1.2200', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-09-20', 'DWJZ': '0.2221', 'JZZZL': '0', 'LJJZ': '1.1650', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-09-19', 'DWJZ': '0.2299', 'JZZZL': '0', 'LJJZ': '1.2650', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-09-18', 'DWJZ': '0.2354', 'JZZZL': '0', 'LJJZ': '1.3570', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-09-17', 'DWJZ': '0.8245', 'JZZZL': '0', 'LJJZ': '1.4550', 'NAVTYPE': '0', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-09-15', 'DWJZ': '0.3917', 'JZZZL': '0', 'LJJZ': '1.4570', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-09-14', 'DWJZ': '0.3310', 'JZZZL': '0', 'LJJZ': '1.5190', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-09-13', 'DWJZ': '0.4141', 'JZZZL': '0', 'LJJZ': '1.5430', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-09-12', 'DWJZ': '0.4065', 'JZZZL': '0', 'LJJZ': '1.5250', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-09-11', 'DWJZ': '0.4233', 'JZZZL': '0', 'LJJZ': '1.5190', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-09-10', 'DWJZ': '0.8278', 'JZZZL': '0', 'LJJZ': '1.5150', 'NAVTYPE': '0', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-09-08', 'DWJZ': '0.5097', 'JZZZL': '0', 'LJJZ': '1.5540', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-09-07', 'DWJZ': '0.3772', 'JZZZL': '0', 'LJJZ': '1.5260', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-09-06', 'DWJZ': '0.3795', 'JZZZL': '0', 'LJJZ': '1.6890', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-09-05', 'DWJZ': '0.3957', 'JZZZL': '0', 'LJJZ': '1.7280', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-09-04', 'DWJZ': '0.4164', 'JZZZL': '0', 'LJJZ': '1.7410', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-09-03', 'DWJZ': '0.9013', 'JZZZL': '0', 'LJJZ': '1.7360', 'NAVTYPE': '0', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-09-01', 'DWJZ': '0.4561', 'JZZZL': '0', 'LJJZ': '1.6850', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-08-31', 'DWJZ': '0.6901', 'JZZZL': '0', 'LJJZ': '1.6560', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-08-30', 'DWJZ': '0.4547', 'JZZZL': '0', 'LJJZ': '1.5060', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-08-29', 'DWJZ': '0.4209', 'JZZZL': '0', 'LJJZ': '1.4800', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-08-28', 'DWJZ': '0.4063', 'JZZZL': '0', 'LJJZ': '1.4740', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-08-27', 'DWJZ': '0.8030', 'JZZZL': '0', 'LJJZ': '1.4800', 'NAVTYPE': '0', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-08-25', 'DWJZ': '0.4016', 'JZZZL': '0', 'LJJZ': '1.5010', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-08-24', 'DWJZ': '0.4019', 'JZZZL': '0', 'LJJZ': '1.5140', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-08-23', 'DWJZ': '0.4052', 'JZZZL': '0', 'LJJZ': '1.7720', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-08-22', 'DWJZ': '0.4083', 'JZZZL': '0', 'LJJZ': '1.8390', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-08-21', 'DWJZ': '0.4188', 'JZZZL': '0', 'LJJZ': '1.8230', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-08-20', 'DWJZ': '0.8422', 'JZZZL': '0', 'LJJZ': '1.8060', 'NAVTYPE': '0', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-08-18', 'DWJZ': '0.4275', 'JZZZL': '0', 'LJJZ': '1.7560', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-08-17', 'DWJZ': '0.8965', 'JZZZL': '0', 'LJJZ': '1.7290', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-08-16', 'DWJZ': '0.5327', 'JZZZL': '0', 'LJJZ': '1.4560', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-08-15', 'DWJZ': '0.3780', 'JZZZL': '0', 'LJJZ': '1.3730', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-08-14', 'DWJZ': '0.3864', 'JZZZL': '0', 'LJJZ': '1.3690', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-08-13', 'DWJZ': '0.7465', 'JZZZL': '0', 'LJJZ': '1.3920', 'NAVTYPE': '0', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-08-11', 'DWJZ': '0.3750', 'JZZZL': '0', 'LJJZ': '1.3900', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-08-10', 'DWJZ': '0.3741', 'JZZZL': '0', 'LJJZ': '1.3870', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-08-09', 'DWJZ': '0.3724', 'JZZZL': '0', 'LJJZ': '1.3670', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-08-08', 'DWJZ': '0.3714', 'JZZZL': '0', 'LJJZ': '1.3800', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-08-07', 'DWJZ': '0.4302', 'JZZZL': '0', 'LJJZ': '1.4040', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-08-06', 'DWJZ': '0.7430', 'JZZZL': '0', 'LJJZ': '1.4180', 'NAVTYPE': '0', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-08-04', 'DWJZ': '0.3696', 'JZZZL': '0', 'LJJZ': '1.4500', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-08-03', 'DWJZ': '0.3351', 'JZZZL': '0', 'LJJZ': '1.4950', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-08-02', 'DWJZ': '0.3976', 'JZZZL': '0', 'LJJZ': '1.5260', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}, {'FSRQ': '2023-08-01', 'DWJZ': '0.4173', 'JZZZL': '0', 'LJJZ': '1.6660', 'NAVTYPE': '1', 'RATE': '--', 'MUI': '--', 'SYI': '--'}], 'ErrCode': 0, 'Success': True, 'ErrMsg': None, 'Message': None, 'ErrorCode': '0', 'ErrorMessage': None, 'ErrorMsgLst': None, 'TotalCount': 3114, 'Expansion': {'COMETHOD': '日结', 'MCOVERDETAIL': '日结', 'CXFS': '一年期定存', 'FundMNChangeList': None, 'BonusList': []}}\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "5448908e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "588460\n",
      "            日期    单位净值    累计净值   涨跌幅\n",
      "0   2023-11-03  0.9058  0.9058  2.12\n",
      "1   2023-11-02  0.8870  0.8870 -1.24\n",
      "2   2023-11-01  0.8981  0.8981 -0.65\n",
      "3   2023-10-31  0.9040  0.9040 -0.35\n",
      "4   2023-10-30  0.9072  0.9072  1.73\n",
      "..         ...     ...     ...   ...\n",
      "70  2023-07-20  0.9759  0.9759 -1.49\n",
      "71  2023-07-19  0.9907  0.9907 -0.47\n",
      "72  2023-07-18  0.9954  0.9954 -0.52\n",
      "73  2023-07-17  1.0006  1.0006 -0.87\n",
      "74  2023-07-14  1.0094  1.0094 -0.44\n",
      "\n",
      "[75 rows x 4 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.009 1.001 0.997 0.991 0.978 0.969 0.964 0.976 0.973 0.961 0.97  0.977\n",
      " 0.974 0.973 0.977 0.981 0.977 0.974 0.963 0.965 0.947 0.937 0.922 0.901\n",
      " 0.912 0.908 0.891 0.903 0.879 0.885 0.867 0.88  0.929 0.953 0.959 0.957\n",
      " 0.957 0.95  0.955 0.924 0.923 0.93  0.927 0.916 0.905 0.906 0.901 0.895\n",
      " 0.889 0.885 0.907 0.902 0.903 0.912 0.922 0.916 0.911 0.923 0.925 0.92\n",
      " 0.907 0.912 0.9   0.9   0.892 0.871 0.88  0.879 0.883 0.893 0.906 0.904\n",
      " 0.899 0.888 0.906] \n",
      " [1.0094 1.0006 0.9954 0.9907 0.9759 0.9682 0.9623 0.9761 0.9728 0.9601\n",
      " 0.9698 0.9756 0.974  0.974  0.9769 0.9823 0.978  0.9744 0.9637 0.965\n",
      " 0.9465 0.9372 0.9231 0.9023 0.9123 0.9053 0.891  0.9007 0.8777 0.885\n",
      " 0.8699 0.8785 0.9226 0.9512 0.9568 0.9513 0.9569 0.9482 0.9537 0.922\n",
      " 0.9233 0.9304 0.9262 0.9161 0.9072 0.9095 0.9016 0.896  0.8875 0.8839\n",
      " 0.9082 0.9013 0.905  0.9109 0.9222 0.9157 0.9111 0.9232 0.9282 0.9197\n",
      " 0.9054 0.9129 0.8993 0.9013 0.8925 0.8708 0.8797 0.8788 0.8819 0.8918\n",
      " 0.9072 0.904  0.8981 0.887  0.9058]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avg_loss: 2.0767066732929607e-10\n",
      "588460 3.65649617838768\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "code_index = -1\n",
    "data_fund = pd.read_excel(path+'/'+result[code_index])\n",
    "up_down = []\n",
    "code_fund = get_have(result[code_index])\n",
    "print(code_fund)\n",
    "data = ef.fund.get_quote_history(code_fund,pz=75)\n",
    "print(data)\n",
    "# public_dates = ef.fund.get_public_dates(code_fund)\n",
    "# kk = ef.fund.get_invest_position(code_fund,dates=public_dates[0])\n",
    "\n",
    "db = ef.stock.get_quote_history(code_fund,beg='20230714',fqt=2)\n",
    "x = np.array(db['收盘'])\n",
    "y = np.array(data['单位净值'][::-1])\n",
    "plt.plot(range(len(x)),x,c='r',label = '收盘(日)')\n",
    "plt.plot(range(len(y)),y,c='b',label='NAV')\n",
    "plt.show()\n",
    "print(x,'\\n',y)\n",
    "# 定义线性回归模型函数\n",
    "\n",
    "# 使用最小二乘法估计参数\n",
    "params, covariance = curve_fit(linear_model, x, y)\n",
    "# 提取估计的模型参数\n",
    "plt.scatter(x, y, label=\"原始数据\")\n",
    "plt.plot(x, linear_model(x, a_est, b_est), color='red', label=\"拟合线\")\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "a_est, b_est = params\n",
    "print('avg_loss:',np.average(linear_model(x, a_est, b_est) - y))\n",
    "get_like = linear_model(data_fund['收盘价'], a_est, b_est)\n",
    "plt.plot(range(len(get_like)), get_like,c = 'r',label = 'IOPV')\n",
    "plt.plot(range(len(get_like)),data_fund['收盘价'],c = 'b',label = '收盘价')\n",
    "plt.legend()\n",
    "plt.plot()\n",
    "# 每只基金的折溢价\n",
    "for i,j in zip(get_like,data_fund['收盘价']):\n",
    "    cha_values = abs(i-j)\n",
    "    if cha_values > 0:\n",
    "        up_down.append(cha_values)\n",
    "sum_values = np.sum(up_down)\n",
    "print(f'{code_fund}',sum_values)\n",
    "end_frame.append([code_fund,sum_values])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                               \r"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\Lenovo\\jupyter_notebook\\Untitled.ipynb Cell 20\u001b[0m line \u001b[0;36m3\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Lenovo/jupyter_notebook/Untitled.ipynb#X16sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39makshare\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mak\u001b[39;00m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/Lenovo/jupyter_notebook/Untitled.ipynb#X16sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m fund_fh_em_df \u001b[39m=\u001b[39m ak\u001b[39m.\u001b[39;49mfund_fh_em()\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Lenovo/jupyter_notebook/Untitled.ipynb#X16sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m \u001b[39mprint\u001b[39m(fund_fh_em_df[(fund_fh_em_df[\u001b[39m'\u001b[39m\u001b[39m基金代码\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m==\u001b[39m\u001b[39m'\u001b[39m\u001b[39m159972\u001b[39m\u001b[39m'\u001b[39m)\u001b[39m.\u001b[39mindex])\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Lenovo/jupyter_notebook/Untitled.ipynb#X16sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m fund_name_em_df \u001b[39m=\u001b[39m ak\u001b[39m.\u001b[39mfund_name_em()\n",
      "File \u001b[1;32me:\\python11\\venv\\Lib\\site-packages\\akshare\\fund\\fund_fhsp_em.py:36\u001b[0m, in \u001b[0;36mfund_fh_em\u001b[1;34m()\u001b[0m\n\u001b[0;32m     34\u001b[0m \u001b[39mfor\u001b[39;00m page \u001b[39min\u001b[39;00m tqdm(\u001b[39mrange\u001b[39m(\u001b[39m1\u001b[39m, total_page \u001b[39m+\u001b[39m \u001b[39m1\u001b[39m), leave\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m):\n\u001b[0;32m     35\u001b[0m     params\u001b[39m.\u001b[39mupdate({\u001b[39m\"\u001b[39m\u001b[39mpage\u001b[39m\u001b[39m\"\u001b[39m: page})\n\u001b[1;32m---> 36\u001b[0m     r \u001b[39m=\u001b[39m requests\u001b[39m.\u001b[39;49mget(url, params\u001b[39m=\u001b[39;49mparams)\n\u001b[0;32m     37\u001b[0m     data_text \u001b[39m=\u001b[39m r\u001b[39m.\u001b[39mtext\n\u001b[0;32m     38\u001b[0m     temp_list \u001b[39m=\u001b[39m \u001b[39meval\u001b[39m(\n\u001b[0;32m     39\u001b[0m         data_text[data_text\u001b[39m.\u001b[39mfind(\u001b[39m\"\u001b[39m\u001b[39m[[\u001b[39m\u001b[39m\"\u001b[39m): data_text\u001b[39m.\u001b[39mfind(\u001b[39m\"\u001b[39m\u001b[39m;var jjfh_jjgs\u001b[39m\u001b[39m\"\u001b[39m)]\n\u001b[0;32m     40\u001b[0m     )\n",
      "File \u001b[1;32me:\\python11\\venv\\Lib\\site-packages\\requests\\api.py:73\u001b[0m, in \u001b[0;36mget\u001b[1;34m(url, params, **kwargs)\u001b[0m\n\u001b[0;32m     62\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mget\u001b[39m(url, params\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m     63\u001b[0m \u001b[39m    \u001b[39m\u001b[39mr\u001b[39m\u001b[39m\"\"\"Sends a GET request.\u001b[39;00m\n\u001b[0;32m     64\u001b[0m \n\u001b[0;32m     65\u001b[0m \u001b[39m    :param url: URL for the new :class:`Request` object.\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     70\u001b[0m \u001b[39m    :rtype: requests.Response\u001b[39;00m\n\u001b[0;32m     71\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[1;32m---> 73\u001b[0m     \u001b[39mreturn\u001b[39;00m request(\u001b[39m\"\u001b[39;49m\u001b[39mget\u001b[39;49m\u001b[39m\"\u001b[39;49m, url, params\u001b[39m=\u001b[39;49mparams, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[1;32me:\\python11\\venv\\Lib\\site-packages\\requests\\api.py:59\u001b[0m, in \u001b[0;36mrequest\u001b[1;34m(method, url, **kwargs)\u001b[0m\n\u001b[0;32m     55\u001b[0m \u001b[39m# By using the 'with' statement we are sure the session is closed, thus we\u001b[39;00m\n\u001b[0;32m     56\u001b[0m \u001b[39m# avoid leaving sockets open which can trigger a ResourceWarning in some\u001b[39;00m\n\u001b[0;32m     57\u001b[0m \u001b[39m# cases, and look like a memory leak in others.\u001b[39;00m\n\u001b[0;32m     58\u001b[0m \u001b[39mwith\u001b[39;00m sessions\u001b[39m.\u001b[39mSession() \u001b[39mas\u001b[39;00m session:\n\u001b[1;32m---> 59\u001b[0m     \u001b[39mreturn\u001b[39;00m session\u001b[39m.\u001b[39;49mrequest(method\u001b[39m=\u001b[39;49mmethod, url\u001b[39m=\u001b[39;49murl, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[1;32me:\\python11\\venv\\Lib\\site-packages\\requests\\sessions.py:589\u001b[0m, in \u001b[0;36mSession.request\u001b[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[0;32m    584\u001b[0m send_kwargs \u001b[39m=\u001b[39m {\n\u001b[0;32m    585\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mtimeout\u001b[39m\u001b[39m\"\u001b[39m: timeout,\n\u001b[0;32m    586\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mallow_redirects\u001b[39m\u001b[39m\"\u001b[39m: allow_redirects,\n\u001b[0;32m    587\u001b[0m }\n\u001b[0;32m    588\u001b[0m send_kwargs\u001b[39m.\u001b[39mupdate(settings)\n\u001b[1;32m--> 589\u001b[0m resp \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msend(prep, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49msend_kwargs)\n\u001b[0;32m    591\u001b[0m \u001b[39mreturn\u001b[39;00m resp\n",
      "File \u001b[1;32me:\\python11\\venv\\Lib\\site-packages\\requests\\sessions.py:703\u001b[0m, in \u001b[0;36mSession.send\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m    700\u001b[0m start \u001b[39m=\u001b[39m preferred_clock()\n\u001b[0;32m    702\u001b[0m \u001b[39m# Send the request\u001b[39;00m\n\u001b[1;32m--> 703\u001b[0m r \u001b[39m=\u001b[39m adapter\u001b[39m.\u001b[39;49msend(request, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[0;32m    705\u001b[0m \u001b[39m# Total elapsed time of the request (approximately)\u001b[39;00m\n\u001b[0;32m    706\u001b[0m elapsed \u001b[39m=\u001b[39m preferred_clock() \u001b[39m-\u001b[39m start\n",
      "File \u001b[1;32me:\\python11\\venv\\Lib\\site-packages\\requests\\adapters.py:486\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    483\u001b[0m     timeout \u001b[39m=\u001b[39m TimeoutSauce(connect\u001b[39m=\u001b[39mtimeout, read\u001b[39m=\u001b[39mtimeout)\n\u001b[0;32m    485\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m--> 486\u001b[0m     resp \u001b[39m=\u001b[39m conn\u001b[39m.\u001b[39;49murlopen(\n\u001b[0;32m    487\u001b[0m         method\u001b[39m=\u001b[39;49mrequest\u001b[39m.\u001b[39;49mmethod,\n\u001b[0;32m    488\u001b[0m         url\u001b[39m=\u001b[39;49murl,\n\u001b[0;32m    489\u001b[0m         body\u001b[39m=\u001b[39;49mrequest\u001b[39m.\u001b[39;49mbody,\n\u001b[0;32m    490\u001b[0m         headers\u001b[39m=\u001b[39;49mrequest\u001b[39m.\u001b[39;49mheaders,\n\u001b[0;32m    491\u001b[0m         redirect\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[0;32m    492\u001b[0m         assert_same_host\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[0;32m    493\u001b[0m         preload_content\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[0;32m    494\u001b[0m         decode_content\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[0;32m    495\u001b[0m         retries\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmax_retries,\n\u001b[0;32m    496\u001b[0m         timeout\u001b[39m=\u001b[39;49mtimeout,\n\u001b[0;32m    497\u001b[0m         chunked\u001b[39m=\u001b[39;49mchunked,\n\u001b[0;32m    498\u001b[0m     )\n\u001b[0;32m    500\u001b[0m \u001b[39mexcept\u001b[39;00m (ProtocolError, \u001b[39mOSError\u001b[39;00m) \u001b[39mas\u001b[39;00m err:\n\u001b[0;32m    501\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mConnectionError\u001b[39;00m(err, request\u001b[39m=\u001b[39mrequest)\n",
      "File \u001b[1;32me:\\python11\\venv\\Lib\\site-packages\\urllib3\\connectionpool.py:714\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    711\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_prepare_proxy(conn)\n\u001b[0;32m    713\u001b[0m \u001b[39m# Make the request on the httplib connection object.\u001b[39;00m\n\u001b[1;32m--> 714\u001b[0m httplib_response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_make_request(\n\u001b[0;32m    715\u001b[0m     conn,\n\u001b[0;32m    716\u001b[0m     method,\n\u001b[0;32m    717\u001b[0m     url,\n\u001b[0;32m    718\u001b[0m     timeout\u001b[39m=\u001b[39;49mtimeout_obj,\n\u001b[0;32m    719\u001b[0m     body\u001b[39m=\u001b[39;49mbody,\n\u001b[0;32m    720\u001b[0m     headers\u001b[39m=\u001b[39;49mheaders,\n\u001b[0;32m    721\u001b[0m     chunked\u001b[39m=\u001b[39;49mchunked,\n\u001b[0;32m    722\u001b[0m )\n\u001b[0;32m    724\u001b[0m \u001b[39m# If we're going to release the connection in ``finally:``, then\u001b[39;00m\n\u001b[0;32m    725\u001b[0m \u001b[39m# the response doesn't need to know about the connection. Otherwise\u001b[39;00m\n\u001b[0;32m    726\u001b[0m \u001b[39m# it will also try to release it and we'll have a double-release\u001b[39;00m\n\u001b[0;32m    727\u001b[0m \u001b[39m# mess.\u001b[39;00m\n\u001b[0;32m    728\u001b[0m response_conn \u001b[39m=\u001b[39m conn \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m release_conn \u001b[39melse\u001b[39;00m \u001b[39mNone\u001b[39;00m\n",
      "File \u001b[1;32me:\\python11\\venv\\Lib\\site-packages\\urllib3\\connectionpool.py:466\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[1;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[0;32m    461\u001b[0m             httplib_response \u001b[39m=\u001b[39m conn\u001b[39m.\u001b[39mgetresponse()\n\u001b[0;32m    462\u001b[0m         \u001b[39mexcept\u001b[39;00m \u001b[39mBaseException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m    463\u001b[0m             \u001b[39m# Remove the TypeError from the exception chain in\u001b[39;00m\n\u001b[0;32m    464\u001b[0m             \u001b[39m# Python 3 (including for exceptions like SystemExit).\u001b[39;00m\n\u001b[0;32m    465\u001b[0m             \u001b[39m# Otherwise it looks like a bug in the code.\u001b[39;00m\n\u001b[1;32m--> 466\u001b[0m             six\u001b[39m.\u001b[39;49mraise_from(e, \u001b[39mNone\u001b[39;49;00m)\n\u001b[0;32m    467\u001b[0m \u001b[39mexcept\u001b[39;00m (SocketTimeout, BaseSSLError, SocketError) \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m    468\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_raise_timeout(err\u001b[39m=\u001b[39me, url\u001b[39m=\u001b[39murl, timeout_value\u001b[39m=\u001b[39mread_timeout)\n",
      "File \u001b[1;32m<string>:3\u001b[0m, in \u001b[0;36mraise_from\u001b[1;34m(value, from_value)\u001b[0m\n",
      "File \u001b[1;32me:\\python11\\venv\\Lib\\site-packages\\urllib3\\connectionpool.py:461\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[1;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[0;32m    458\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[0;32m    459\u001b[0m     \u001b[39m# Python 3\u001b[39;00m\n\u001b[0;32m    460\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m--> 461\u001b[0m         httplib_response \u001b[39m=\u001b[39m conn\u001b[39m.\u001b[39;49mgetresponse()\n\u001b[0;32m    462\u001b[0m     \u001b[39mexcept\u001b[39;00m \u001b[39mBaseException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m    463\u001b[0m         \u001b[39m# Remove the TypeError from the exception chain in\u001b[39;00m\n\u001b[0;32m    464\u001b[0m         \u001b[39m# Python 3 (including for exceptions like SystemExit).\u001b[39;00m\n\u001b[0;32m    465\u001b[0m         \u001b[39m# Otherwise it looks like a bug in the code.\u001b[39;00m\n\u001b[0;32m    466\u001b[0m         six\u001b[39m.\u001b[39mraise_from(e, \u001b[39mNone\u001b[39;00m)\n",
      "File \u001b[1;32mC:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.11_3.11.1776.0_x64__qbz5n2kfra8p0\\Lib\\http\\client.py:1378\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1376\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m   1377\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m-> 1378\u001b[0m         response\u001b[39m.\u001b[39;49mbegin()\n\u001b[0;32m   1379\u001b[0m     \u001b[39mexcept\u001b[39;00m \u001b[39mConnectionError\u001b[39;00m:\n\u001b[0;32m   1380\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mclose()\n",
      "File \u001b[1;32mC:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.11_3.11.1776.0_x64__qbz5n2kfra8p0\\Lib\\http\\client.py:318\u001b[0m, in \u001b[0;36mHTTPResponse.begin\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    316\u001b[0m \u001b[39m# read until we get a non-100 response\u001b[39;00m\n\u001b[0;32m    317\u001b[0m \u001b[39mwhile\u001b[39;00m \u001b[39mTrue\u001b[39;00m:\n\u001b[1;32m--> 318\u001b[0m     version, status, reason \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_read_status()\n\u001b[0;32m    319\u001b[0m     \u001b[39mif\u001b[39;00m status \u001b[39m!=\u001b[39m CONTINUE:\n\u001b[0;32m    320\u001b[0m         \u001b[39mbreak\u001b[39;00m\n",
      "File \u001b[1;32mC:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.11_3.11.1776.0_x64__qbz5n2kfra8p0\\Lib\\http\\client.py:279\u001b[0m, in \u001b[0;36mHTTPResponse._read_status\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    278\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_read_status\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m--> 279\u001b[0m     line \u001b[39m=\u001b[39m \u001b[39mstr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfp\u001b[39m.\u001b[39mreadline(_MAXLINE \u001b[39m+\u001b[39m \u001b[39m1\u001b[39m), \u001b[39m\"\u001b[39m\u001b[39miso-8859-1\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m    280\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(line) \u001b[39m>\u001b[39m _MAXLINE:\n\u001b[0;32m    281\u001b[0m         \u001b[39mraise\u001b[39;00m LineTooLong(\u001b[39m\"\u001b[39m\u001b[39mstatus line\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "File \u001b[1;32mC:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.11_3.11.1776.0_x64__qbz5n2kfra8p0\\Lib\\socket.py:706\u001b[0m, in \u001b[0;36mSocketIO.readinto\u001b[1;34m(self, b)\u001b[0m\n\u001b[0;32m    704\u001b[0m \u001b[39mwhile\u001b[39;00m \u001b[39mTrue\u001b[39;00m:\n\u001b[0;32m    705\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m--> 706\u001b[0m         \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_sock\u001b[39m.\u001b[39;49mrecv_into(b)\n\u001b[0;32m    707\u001b[0m     \u001b[39mexcept\u001b[39;00m timeout:\n\u001b[0;32m    708\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_timeout_occurred \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [分红, 除息日期]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "# print(fund_fh_em_df)\n",
    "print(str(fund_fh_em_df[['分红','除息日期']].loc[fund_fh_em_df['基金代码']=='159001']))\n",
    "# print(fund_fh_em_df['分红'][fund_fh_em_df[fund_fh_em_df['基金代码']=='159972'].index])\n",
    "# print(fund_fh_em_df['除息日期'][fund_fh_em_df[fund_fh_em_df['基金代码']=='159972'].index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
